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    -

    I am a senior staff research scientist at Google Research in San Francisco, where I work on computer vision and machine learning. +

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof Nathan Wiebe. My research interests are reinforcement learning, quantum computing.

    At Google I've worked on Glass, Lens Blur, HDR+, Jump, Portrait Mode, Portrait Light, and NeRF. I did my PhD at UC Berkeley, where I was advised by Jitendra Malik and funded by the NSF GRFP. I've received the C.V. Ramamoorthy Distinguished Research Award and the PAMI Young Researcher Award.

    - Email  /  + Email  /  CV  /  Bio  /  Google Scholar  /  @@ -35,7 +35,7 @@

    - profile photo + profile photo @@ -44,7 +44,7 @@ Research

    - I'm interested in computer vision, machine learning, optimization, and image processing. Much of my research is about inferring the physical world (shape, motion, color, light, etc) from images. Representative papers are highlighted. + I'm interested in reinforcement learning and quantum algorithms. Much of my research is about developing novel reinforcement learning and quantum computing algorithms.

    From 0f661e1c4ebbd12dadc66d6ee818965be88914d2 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Thu, 7 Dec 2023 23:12:04 -0500 Subject: [PATCH 02/33] deleted most of Jon's research paper links --- index.html | 3389 +--------------------------------------------------- 1 file changed, 7 insertions(+), 3382 deletions(-) diff --git a/index.html b/index.html index 22e7ca5ce8..9398722175 100755 --- a/index.html +++ b/index.html @@ -1,9 +1,9 @@ - Jon Barron + Abhinav Muraleedharan - + @@ -18,20 +18,20 @@

    - Jon Barron + Abhinav Muraleedharan

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof Nathan Wiebe. My research interests are reinforcement learning, quantum computing.

    - At Google I've worked on Glass, Lens Blur, HDR+, Jump, Portrait Mode, Portrait Light, and NeRF. I did my PhD at UC Berkeley, where I was advised by Jitendra Malik and funded by the NSF GRFP. I've received the C.V. Ramamoorthy Distinguished Research Award and the PAMI Young Researcher Award. + I invented Score-life programming.

    Email  /  CV  /  Bio  /  Google Scholar  /  - Twitter  /  - Github + Twitter  /  + Github

    @@ -50,3352 +50,8 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
    -
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    - -
    - - BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis - -
    - Lior Yariv*, - Peter Hedman*, - Christian Reiser, - Dor Verbin,
    - Pratul Srinivasan, - Richard Szeliski, - Jonathan T. Barron, - Ben Mildenhall -
    - SIGGRAPH, 2023 -
    - project page - / - video - / - arXiv -

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    - We use SDFs to bake a NeRF-like model into a high quality mesh and do real-time view synthesis. -

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    - -
    - - MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes - -
    - Christian Reiser, - Richard Szeliski, - Dor Verbin, - Pratul Srinivasan,
    - Ben Mildenhall, - Andreas Geiger, - Jonathan T. Barron, - Peter Hedman -
    - SIGGRAPH, 2023 -
    - project page - / - video - / - arXiv -

    -

    - We use volumetric rendering with a sparse 3D feature grid and 2D feature planes to do real-time view synthesis. -

    -
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    - -
    - - Eclipse: Disambiguating Illumination and Materials using Unintended Shadows - -
    - Dor Verbin, - Ben Mildenhall, - Peter Hedman,
    - Jonathan T. Barron, - Todd Zickler, - Pratul Srinivasan -
    - arXiv, 2023 -
    - project page - / - video - / - arXiv -

    -

    - Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials. -

    -
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    - -
    - -
    - - Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields - -
    - Jonathan T. Barron, - Ben Mildenhall, - Dor Verbin, - Pratul Srinivasan, - Peter Hedman -
    - arXiv, 2023 -
    - project page - / - video - / - arXiv -

    -

    - Combining mip-NeRF 360 and grid-based models like Instant NGP lets us reduce error rates by 8%–77% and accelerate training by 24x. -

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    - - DreamBooth3D: Subject-Driven Text-to-3D Generation - -
    - -Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, -Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, - Jonathan T. Barron, Yuanzhen Li, Varun Jampani -
    - arXiv, 2023 -
    - project page / - arXiv -

    -

    Combining DreamBooth (personalized text-to-image) and DreamFusion (text-to-3D) yields high-quality, subject-specific 3D assets with text-driven modifications

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    - -
    - -
    - - AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training - - -
    - Yifan Jiang, - Peter Hedman, - Ben Mildenhall, - Dejia Xu,
    - Jonathan T. Barron, - Zhangyang Wang, - Tianfan Xue -
    - CVPR, 2023 -
    - project page - / - arXiv -

    -

    - Accounting for misalignment due to scene motion or calibration errors improves NeRF reconstruction quality. -

    -
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    - -
    - -
    - - DreamFusion: Text-to-3D using 2D Diffusion - -
    - Ben Poole, - Ajay Jain, - Jonathan T. Barron, - Ben Mildenhall -
    - ICLR, 2023   (Oral Presentation, Outstanding Paper Award) -
    - project page - / - arXiv - / - gallery -

    -

    - We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling. -

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    - - MIRA: Mental Imagery for Robotic Affordances - -
    - Lin Yen-Chen, - Pete Florence, - Andy Zeng, Jonathan T. Barron, - Yilun Du, - Wei-Chiu Ma, - Anthony Simeonov, - Alberto Rodriguez, - Phillip Isola -
    - CoRL, 2022 -

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    - NeRF lets us synthesize novel orthographic views that work well with pixel-wise algorithms for robotic manipulation. -

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    - - SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections - -
    - Mark Boss, - Andreas Engelhardt, - Abhishek Kar, - Yuanzhen Li, - Deqing Sun, - Jonathan T. Barron, - Hendrik P. A. Lensch, - Varun Jampani -
    - NeurIPS, 2022 -
    - project page / - video / - arXiv -

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    -A joint optimization framework for estimating shape, BRDF, camera pose, and illumination from in-the-wild image collections. -

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    - - Polynomial Neural Fields for Subband Decomposition -
    - Guandao Yang*, - Sagie Benaim*, - Varun Jampani, - Kyle Genova, - Jonathan T. Barron, - Thomas Funkhouser, - Bharath Hariharan, - Serge Belongie -
    - NeurIPS, 2022 -

    - Representing neural fields as a composition of manipulable and interpretable components lets you do things like reason about frequencies and scale. -

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    - - Fast and High-Quality Image Denoising via Malleable Convolutions - -
    - Yifan Jiang, - Bartlomiej Wronski, - Ben Mildenhall,
    - Jonathan T. Barron, - Zhangyang Wang, - Tianfan Xue -
    - ECCV, 2022 -
    - project page - / - arXiv -

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    - We denoise images efficiently by predicting spatially-varying kernels at low resolution and using a fast fused op to jointly upsample and apply these kernels at full resolution. -

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    - - NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields - -
    - Lin Yen-Chen, - Pete Florence, - Jonathan T. Barron,
    - Tsung-Yi Lin, - Alberto Rodriguez, - Phillip Isola -
    - ICRA, 2022 -
    - project page / - arXiv / - video / - code / - colab -

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    NeRF works better than RGB-D cameras or multi-view stereo when learning object descriptors.

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    - - Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields - -
    - Dor Verbin, - Peter Hedman, - Ben Mildenhall,
    - Todd Zickler, - Jonathan T. Barron, - Pratul Srinivasan -
    - CVPR, 2022   (Oral Presentation, Best Student Paper Honorable Mention) -
    - project page - / - arXiv - / - video -

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    Explicitly modeling reflections in NeRF produces realistic shiny surfaces and accurate surface normals, and lets you edit materials.

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    - - Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields - -
    - Jonathan T. Barron, - Ben Mildenhall, - Dor Verbin, - Pratul Srinivasan, - Peter Hedman -
    - CVPR, 2022   (Oral Presentation) -
    - project page - / - arXiv - / - video -

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    mip-NeRF can be extended to produce realistic results on unbounded scenes.

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    - - NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images - -
    - Ben Mildenhall, - Peter Hedman, - Ricardo Martin-Brualla,
    - Pratul Srinivasan, - Jonathan T. Barron -
    - CVPR, 2022   (Oral Presentation) -
    - project page - / - arXiv - / - video -

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    - Properly training NeRF on raw camera data enables HDR view synthesis and bokeh, and outperforms multi-image denoising.

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    - - RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs - -
    - Michael Niemeyer, - Jonathan T. Barron, - Ben Mildenhall,
    - Mehdi S. M. Sajjadi, - Andreas Geiger, - Noha Radwan -
    - CVPR, 2022   (Oral Presentation) -
    - project page - / - arXiv - / - video -

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    Regularizing unseen views during optimization enables view synthesis from as few as 3 input images.

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    - - Block-NeRF: Scalable Large Scene Neural View Synthesis - -
    - Matthew Tancik, - Vincent Casser, - Xinchen Yan, - Sabeek Pradhan,
    - Ben Mildenhall, - Pratul Srinivasan, - Jonathan T. Barron, - Henrik Kretzschmar -
    - CVPR, 2022   (Oral Presentation) -
    - project page - / - arXiv - / - video -

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    We can do city-scale reconstruction by training multiple NeRFs with millions of images.

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    - - HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video - -
    - Chung-Yi Weng, - Brian Curless, - Pratul Srinivasan,
    - Jonathan T. Barron, - Ira Kemelmacher-Shlizerman -
    - CVPR, 2022   (Oral Presentation) -
    - project page - / - arXiv - / - video -

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    Combining NeRF with pose estimation lets you use a monocular video to do free-viewpoint rendering of a human.

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    - - Urban Radiance Fields - -
    - Konstantinos Rematas, - Andrew Liu, - Pratul P. Srinivasan, - Jonathan T. Barron,
    - Andrea Tagliasacchi, - Tom Funkhouser, - Vittorio Ferrari -
    - CVPR, 2022 -
    - project page - / - arXiv - / - video -

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    - Incorporating lidar and explicitly modeling the sky lets you reconstruct urban environments.

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    - - Dense Depth Priors for Neural Radiance Fields from Sparse Input Views - -
    - Barbara Roessle, - Jonathan T. Barron, - Ben Mildenhall, - Pratul Srinivasan, - Matthias Nießner -
    - CVPR, 2022 -
    - arXiv - / - video -

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    - Dense depth completion techniques applied to freely-available sparse stereo data can improve NeRF reconstructions in low-data regimes. -

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    - - Zero-Shot Text-Guided Object Generation with Dream Fields - -
    - Ajay Jain, - Ben Mildenhall, - Jonathan T. Barron, - Pieter Abbeel, - Ben Poole -
    - CVPR, 2022 -
    - project page - / - arXiv - / - video -

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    Supervising the CLIP embeddings of NeRF renderings lets you to generate 3D objects from text prompts.

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    - - Advances in Neural Rendering - -
    - Ayush Tewari, - Justus Thies, - Ben Mildenhall, - Pratul Srinivasan, - Edgar Tretschk, - Yifan Wang, - Christoph Lassner, - Vincent Sitzmann, - Ricardo Martin-Brualla, - Stephen Lombardi, - Tomas Simon, - Christian Theobalt, - Matthias Niessner, - Jonathan T. Barron, - Gordon Wetzstein, - Michael Zollhoefer, - Vladislav Golyanik -
    - Arxiv, 2021 -
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    - A survey of recent progress in neural rendering. -

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    - - Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition - -
    - - Mark Boss, - Varun Jampani, - Raphael Braun,
    - Ce Liu, - Jonathan T. Barron, - Hendrik P. A. Lensch -
    - NeurIPS, 2021 -
    - project page / - video / - arXiv -

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    - Replacing a costly illumination integral with a simple network query enables more accurate novel view-synthesis and relighting compared to NeRD. -

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    - - HyperNeRF: A Higher-Dimensional Representation -for Topologically Varying Neural Radiance Fields - -
    - Keunhong Park, - Utkarsh Sinha, - Peter Hedman, - Jonathan T. Barron,
    - Sofien Bouaziz, - Dan B Goldman, - Ricardo Martin-Brualla, - Steven M. Seitz -
    - SIGGRAPH Asia, 2021 -
    - project page - / - arXiv -

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    Applying ideas from level set methods to NeRF lets you represent scenes that deform and change shape.

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    - - NeRFactor: Neural Factorization of Shape and Reflectance
    -Under an Unknown Illumination
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    - Xiuming Zhang, - Pratul Srinivasan, - Boyang Deng,
    - Paul Debevec, - William T. Freeman, - Jonathan T. Barron -
    - SIGGRAPH Asia, 2021 -
    - project page - / - arXiv - / - video -

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    By placing priors on illumination and materials, we can recover NeRF-like models of the intrinsics of a scene from a single multi-image capture.

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    - - Scalable Font Reconstruction with Dual Latent Manifolds - -
    - Nikita Srivatsan, - Si Wu, - Jonathan T. Barron, - Taylor Berg-Kirkpatrick -
    - EMNLP, 2021 -
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    VAEs can be used to disentangle a font's style from its content, and to generalize to characters that were never observed during training.

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    - - Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields - -
    - Jonathan T. Barron, - Ben Mildenhall, - Matthew Tancik,
    - Peter Hedman, - Ricardo Martin-Brualla, - Pratul Srinivasan -
    - ICCV, 2021   (Oral Presentation, Best Paper Honorable Mention) -
    - project page - / - arXiv - / - video - / - code -

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    NeRF is aliased, but we can anti-alias it by casting cones and prefiltering the positional encoding function.

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    - - Baking Neural Radiance Fields for Real-Time View Synthesis - -
    - Peter Hedman, - Pratul Srinivasan, - Ben Mildenhall, - Jonathan T. Barron, - Paul Debevec -
    - ICCV, 2021   (Oral Presentation) -
    - project page - / - arXiv - / - video - / - demo -

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    Baking a trained NeRF into a sparse voxel grid of colors and features lets you render it in real-time in your browser.

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    - -
    - - Nerfies: Deformable Neural Radiance Fields - -
    - - Keunhong Park, - Utkarsh Sinha, - Jonathan T. Barron,
    - Sofien Bouaziz, - Dan B Goldman, - Steven M. Seitz, - Ricardo-Martin Brualla -
    - ICCV, 2021   (Oral Presentation) -
    - project page / - arXiv / - video -

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    Building deformation fields into NeRF lets you capture non-rigid subjects, like people. -

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    - - Cross-Camera Convolutional Color Constancy - -
    - Mahmoud Afifi, - Jonathan T. Barron, - Chloe LeGendre, - Yun-Ta Tsai, - Francois Bleibel -
    - ICCV, 2021   (Oral Presentation) -
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    - With some extra (unlabeled) test-set images, you can build a hypernetwork that calibrates itself at test time to previously-unseen cameras. -

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    - - Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image - -
    - Shumian Xin, - Neal Wadhwa, - Tianfan Xue, - Jonathan T. Barron,
    - Pratul Srinivasan, - Jiawen Chen, - Ioannis Gkioulekas, - Rahul Garg -
    - ICCV, 2021   (Oral Presentation) -
    - project page / - code -
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    - Multiplane images can be used to simultaneously deblur dual-pixel images, despite variable defocus due to depth variation in the scene. -

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    - - NeRD: Neural Reflectance Decomposition from Image Collections - -
    - - Mark Boss, - Raphael Braun, - Varun Jampani, - Jonathan T. Barron, - Ce Liu, - Hendrik P. A. Lensch -
    - ICCV, 2021 -
    - project page / - video / - code / - arXiv -

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    - A NeRF-like model that can decompose (and mesh) objects with non-Lambertian reflectances, complex geometry, and unknown illumination. -

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    - - How to Train Neural Networks for Flare Removal - -
    - Yicheng Wu, - Qiurui He, - Tianfan Xue, - Rahul Garg,
    - Jiawen Chen, - Ashok Veeraraghavan, - Jonathan T. Barron -
    - ICCV, 2021 -
    - project page / - arXiv -

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    - Simulating the optics of a camera's lens lets you train a model that removes lens flare from a single image. -

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    - - iNeRF: Inverting Neural Radiance Fields for Pose Estimation - -
    - Lin Yen-Chen, - Pete Florence, - Jonathan T. Barron,
    - Alberto Rodriguez, - Phillip Isola, - Tsung-Yi Lin -
    - IROS, 2021 -
    - project page / - arXiv / - video -

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    Given an image of an object and a NeRF of that object, you can estimate that object's pose. -

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    - - IBRNet: Learning Multi-View Image-Based Rendering - -
    - Qianqian Wang, - Zhicheng Wang, - Kyle Genova, - Pratul Srinivasan, - Howard Zhou,
    - Jonathan T. Barron, - Ricardo Martin-Brualla, - Noah Snavely, - Thomas Funkhouser -
    - CVPR, 2021 -
    - project page / - code / - arXiv -

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    By learning how to pay attention to input images at render time, - we can amortize inference for view synthesis and reduce error rates by 15%.

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    - - NeRV: Neural Reflection and Visibility Fields for Relighting and View Synthesis - -
    - Pratul Srinivasan, - Boyang Deng, - Xiuming Zhang,
    - Matthew Tancik, - Ben Mildenhall, - Jonathan T. Barron -
    - CVPR, 2021 -
    - project page / - video / - arXiv -

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    Using neural approximations of expensive visibility integrals lets you recover relightable NeRF-like models.

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    - - Learned Initializations for Optimizing Coordinate-Based Neural Representations - -
    - Matthew Tancik*, - Ben Mildenhall*, - Terrance Wang, - Divi Schmidt,
    - Pratul Srinivasan, - Jonathan T. Barron, - Ren Ng -
    - CVPR, 2021   (Oral Presentation) -
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    Using meta-learning to find weight initializations for coordinate-based MLPs allows them to converge faster and generalize better.

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    - - NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections - -
    - Ricardo Martin-Brualla*, - Noha Radwan*, - Mehdi S. M. Sajjadi*,
    - Jonathan T. Barron, - Alexey Dosovitskiy, - Daniel Duckworth -
    - CVPR, 2021   (Oral Presentation) -
    - project page / - arXiv / - video -

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    Letting NeRF reason about occluders and appearance variation produces photorealistic view synthesis using only unstructured internet photos.

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    - - Learned Dual-View Reflection Removal - -
    - Simon Niklaus, - Xuaner (Cecilia) Zhang, - Jonathan T. Barron,
    - Neal Wadhwa, - Rahul Garg, - Feng Liu, - Tianfan Xue -
    - WACV, 2021 -
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    - Reflections and the things behind them often exhibit parallax, and this lets you remove reflections from stereo pairs. -

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    - - Neural Light Transport for Relighting and View Synthesis - -
    - Xiuming Zhang, - Sean Fanello, - Yun-Ta Tsai, - Tiancheng Sun, - Tianfan Xue, - Rohit Pandey, - Sergio Orts-Escolano, - Philip Davidson, - Christoph Rhemann, - Paul Debevec, - Jonathan T. Barron, - Ravi Ramamoorthi, - William T. Freeman -
    - ACM TOG, 2021 -
    - project page / - arXiv / - video -

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    Embedding a convnet within a predefined texture atlas enables simultaneous view synthesis and relighting.

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    - - Light Stage Super-Resolution: Continuous High-Frequency Relighting - -
    - Tiancheng Sun, - Zexiang Xu - Xiuming Zhang, - Sean Fanello, - Christoph Rhemann,
    - Paul Debevec, - Yun-Ta Tsai, - Jonathan T. Barron, - Ravi Ramamoorthi -
    - SIGGRAPH Asia, 2020 -
    - project page / - arXiv -

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    - Scans for light stages are inherently aliased, but we can use learning to super-resolve them. -

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    - - Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains - -
    - Matthew Tancik*, - Pratul Srinivasan*, - Ben Mildenhall*, - Sara Fridovich-Keil,
    - Nithin Raghavan, - Utkarsh Singhal, - Ravi Ramamoorthi, - Jonathan T. Barron, - Ren Ng -
    - NeurIPS, 2020   (Spotlight) -
    - project page / - video: 3 min, 10 min / - arXiv / - code -

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    Composing neural networks with a simple Fourier feature mapping allows them to learn detailed high-frequency functions.

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    - - A Generalization of Otsu's Method and Minimum Error Thresholding - -
    - Jonathan T. Barron -
    - ECCV, 2020   (Spotlight) -
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    - A simple and fast Bayesian algorithm that can be written in ~10 lines of code outperforms or matches giant CNNs on image binarization, and unifies three classic thresholding algorithms. -

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    - - What Matters in Unsupervised Optical Flow - -
    - Rico Jonschkowski, - Austin Stone, - Jonathan T. Barron,
    - Ariel Gordon, - Kurt Konolige, - Anelia Angelova -
    - ECCV, 2020   (Oral Presentation) -
    - code -
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    -

    - Extensive experimentation yields a simple optical flow technique that is trained on only unlabeled videos, but still works as well as supervised techniques. -

    -
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    -
    - -
    - -
    - - NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis - -
    - Ben Mildenhall*, - Pratul Srinivasan*, - Matthew Tancik*,
    - Jonathan T. Barron, - Ravi Ramamoorthi, - Ren Ng -
    - ECCV, 2020   (Oral Presentation, Best Paper Honorable Mention, CACM Research Highlight) -
    - project page - / - arXiv - / - talk video - / - supp video - / - code - / - CACM (foreward) -

    -

    - Training a tiny non-convolutional neural network to reproduce a scene using volume rendering achieves photorealistic view synthesis.

    -
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    -
    - -
    - -
    - - Portrait Shadow Manipulation - -
    - Xuaner (Cecilia) Zhang, - Jonathan T. Barron, - Yun-Ta Tsai,
    - Rohit Pandey, - Xiuming Zhang, - Ren Ng, - David E. Jacobs -
    - SIGGRAPH, 2020 -
    - project page / - video -

    -

    Networks can be trained to remove shadows cast on human faces and to soften harsh lighting.

    -
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    -
    - -
    - -
    - - Learning to Autofocus - -
    - Charles Herrmann, - Richard Strong Bowen, - Neal Wadhwa,
    - Rahul Garg, - Qiurui He, - Jonathan T. Barron, - Ramin Zabih -
    - CVPR, 2020 -
    - project page - / - arXiv -

    -

    Machine learning can be used to train cameras to autofocus (which is not the same problem as "depth from defocus").

    -
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    -
    - -
    - -
    - - Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination - -
    - Pratul Srinivasan*, - Ben Mildenhall*, - Matthew Tancik,
    - Jonathan T. Barron, - Richard Tucker, - Noah Snavely -
    - CVPR, 2020 -
    - project page - / - code - / - arXiv - / - video -

    -

    We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.

    -
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    - -
    - -
    - - Sky Optimization: Semantically Aware Image Processing of Skies in Low-Light Photography - -
    - Orly Liba, - Longqi Cai, - Yun-Ta Tsai, - Elad Eban, - Yair Movshovitz-Attias,
    - Yael Pritch, - Huizhong Chen, - Jonathan T. Barron -
    - NTIRE CVPRW, 2020 -
    - project page -

    -

    If you want to photograph the sky, it helps to know where the sky is.

    -
    -
    -
    - -
    - -
    - - Handheld Mobile Photography in Very Low Light - -
    - Orly Liba, - Kiran Murthy, - Yun-Ta Tsai, - Timothy Brooks, - Tianfan Xue, - Nikhil Karnad, - Qiurui He, - Jonathan T. Barron, - Dillon Sharlet, - Ryan Geiss, - Samuel W. Hasinoff, - Yael Pritch, - Marc Levoy -
    - SIGGRAPH Asia, 2019 -
    - project page -
    -

    -

    By rethinking metering, white balance, and tone mapping, we can take pictures in places too dark for humans to see clearly.

    -
    -
    -
    - -
    - -
    - - A Deep Factorization of Style and Structure in Fonts - -
    - Nikita Srivatsan, - Jonathan T. Barron, - Dan Klein, - Taylor Berg-Kirkpatrick -
    - EMNLP, 2019   (Oral Presentation) -
    -

    -

    Variational auto-encoders can be used to disentangle a characters style from its content.

    -
    -
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    - -
    - -
    - - Learning Single Camera Depth Estimation using Dual-Pixels - -
    - Rahul Garg, - Neal Wadhwa, - Sameer Ansari, - Jonathan T. Barron -
    - ICCV, 2019   (Oral Presentation) -
    - code / - bibtex -

    -

    Considering the optics of dual-pixel image sensors improves monocular depth estimation techniques.

    -
    -
    -
    - -
    - -
    - - Single Image Portrait Relighting - -
    - Tiancheng Sun, - Jonathan T. Barron, - Yun-Ta Tsai, - Zexiang Xu, Xueming Yu, - Graham Fyffe, Christoph Rhemann, Jay Busch, - Paul Debevec, - Ravi Ramamoorthi -
    - SIGGRAPH, 2019 -
    - project page / - arxiv / - video / - press / - bibtex -
    -

    -

    Training a neural network on light stage scans and environment maps produces an effective relighting method.

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    - -
    - -
    - - A General and Adaptive Robust Loss Function - -
    - Jonathan T. Barron -
    - CVPR, 2019   (Oral Presentation, Best Paper Award Finalist) -
    - arxiv / - supplement / - video / - talk / - slides / - code: TF, JAX, pytorch / - reviews / - bibtex -

    -

    A single robust loss function is a superset of many other common robust loss functions, and allows training to automatically adapt the robustness of its own loss.

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    - -
    - -
    - - Pushing the Boundaries of View Extrapolation with Multiplane Images - -
    - Pratul P. Srinivasan, Richard Tucker, - Jonathan T. Barron, - Ravi Ramamoorthi, - Ren Ng, - Noah Snavely -
    - CVPR, 2019   (Oral Presentation, Best Paper Award Finalist) -
    - supplement / - video / - bibtex -

    -

    View extrapolation with multiplane images works better if you reason about disocclusions and disparity sampling frequencies.

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    - -
    - -
    - - Unprocessing Images for Learned Raw Denoising - -
    - Tim Brooks, - Ben Mildenhall, - Tianfan Xue, - Jiawen Chen, - Dillon Sharlet, - Jonathan T. Barron -
    - CVPR, 2019   (Oral Presentation) -
    - arxiv / - project page / - code / - bibtex -

    -

    We can learn a better denoising model by processing and unprocessing images the same way a camera does.

    -
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    - -
    - -
    - - Learning to Synthesize Motion Blur - -
    - Tim Brooks, - Jonathan T. Barron -
    - CVPR, 2019   (Oral Presentation) -
    - arxiv / - supplement / - project page / - video / - code / - bibtex -

    -

    Frame interpolation techniques can be used to train a network that directly synthesizes linear blur kernels.

    -
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    - -
    - -
    - - Stereoscopic Dark Flash for Low-light Photography - -
    - Jian Wang, - Tianfan Xue, - Jonathan T. Barron, - Jiawen Chen -
    - ICCP, 2019 -
    -

    -

    - By making one camera in a stereo pair hyperspectral we can multiplex dark flash pairs in space instead of time. -

    -
    -
    -
    - -
    - -
    - - Depth from Motion for Smartphone AR - -
    - Julien Valentin, - Adarsh Kowdle, - Jonathan T. Barron, Neal Wadhwa, and others -
    - SIGGRAPH Asia, 2018 -
    - planar filter toy code / - bibtex -

    -

    Depth cues from camera motion allow for real-time occlusion effects in augmented reality applications.

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    - -
    - -
    - - Synthetic Depth-of-Field with a Single-Camera Mobile Phone - -
    - Neal Wadhwa, - Rahul Garg, - David E. Jacobs, Bryan E. Feldman, Nori Kanazawa, Robert Carroll, - Yair Movshovitz-Attias, - Jonathan T. Barron, Yael Pritch, - Marc Levoy -
    - SIGGRAPH, 2018 -
    - arxiv / - blog post / - bibtex -

    -

    Dual pixel cameras and semantic segmentation algorithms can be used for shallow depth of field effects.

    -

    This system is the basis for "Portrait Mode" on the Google Pixel 2 smartphones

    -
    -
    -
    - -
    - -
    - - Aperture Supervision for Monocular Depth Estimation - -
    - Pratul P. Srinivasan, - Rahul Garg, - Neal Wadhwa, - Ren Ng, - Jonathan T. Barron -
    - CVPR, 2018 -
    - code / - bibtex -

    -

    Varying a camera's aperture provides a supervisory signal that can teach a neural network to do monocular depth estimation.

    -
    -
    -
    - -
    - -
    - - Burst Denoising with Kernel Prediction Networks - -
    - Ben Mildenhall, - Jonathan T. Barron, - Jiawen Chen, - Dillon Sharlet, - Ren Ng, Robert Carroll -
    - CVPR, 2018   (Spotlight) -
    - supplement / - code / - bibtex -

    -

    We train a network to predict linear kernels that denoise noisy bursts from cellphone cameras.

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    - -
    - -
    - - A Hardware-Friendly Bilateral Solver for Real-Time Virtual Reality Video - -
    - Amrita Mazumdar, Armin Alaghi, Jonathan T. Barron, David Gallup, Luis Ceze, Mark Oskin, Steven M. Seitz -
    - High-Performance Graphics (HPG), 2017 -
    - project page -

    -

    A reformulation of the bilateral solver can be implemented efficiently on GPUs and FPGAs.

    -
    -
    -
    - -
    - -
    - - Deep Bilateral Learning for Real-Time Image Enhancement - -
    - Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand -
    - SIGGRAPH, 2017 -
    - project page / - video / - bibtex / - press -

    -

    By training a deep network in bilateral space we can learn a model for high-resolution and real-time image enhancement.

    -
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    -
    - -
    - -
    - - Fast Fourier Color Constancy - -
    - Jonathan T. Barron, - Yun-Ta Tsai, -
    - CVPR, 2017 -
    - video / - bibtex / - code / - output / - blog post / - press -

    -

    Color space can be aliased, allowing white balance models to be learned and evaluated in the frequency domain. This improves accuracy by 13-20% and speed by 250-3000x.

    -

    This technology is used by Google Pixel, Google Photos, and Google Maps.

    -
    -
    -
    - -
    - -
    - - Jump: Virtual Reality Video - -
    - Robert Anderson, David Gallup, Jonathan T. Barron, Janne Kontkanen, Noah Snavely, Carlos Hernández, Sameer Agarwal, Steven M Seitz -
    - SIGGRAPH Asia, 2016 -
    - supplement / - video / - bibtex / - blog post -

    -

    Using computer vision and a ring of cameras, we can make video for virtual reality headsets that is both stereo and 360°.

    -

    This technology is used by Jump.

    -
    -
    -
    - -
    - -
    - - Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras - -
    - Samuel W. Hasinoff, Dillon Sharlet, Ryan Geiss, Andrew Adams, Jonathan T. Barron, Florian Kainz, Jiawen Chen, Marc Levoy -
    - SIGGRAPH Asia, 2016 -
    - project page / - supplement / - bibtex -

    -

    Mobile phones can take beautiful photographs in low-light or high dynamic range environments by aligning and merging a burst of images.

    -

    This technology is used by the Nexus HDR+ feature.

    -
    -
    -
    - -
    - -
    - - The Fast Bilateral Solver - -
    - Jonathan T. Barron, - Ben Poole -
    - ECCV, 2016   (Oral Presentation, Best Paper Honorable Mention) -
    - arXiv / - bibtex / - video (they messed up my slides, use →) / - keynote (or PDF) / - code / - depth super-res results / - reviews -

    -

    Our solver smooths things better than other filters and faster than other optimization algorithms, and you can backprop through it.

    -
    -
    -
    - -
    - -
    - - Geometric Calibration for Mobile, Stereo, Autofocus Cameras - -
    - Stephen DiVerdi, - Jonathan T. Barron -
    - WACV, 2016 -
    - bibtex -

    -

    Standard techniques for stereo calibration don't work for cheap mobile cameras.

    -
    -
    -
    - -
    - -
    - - Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform - -
    - CVPR, 2016 -
    - Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille -
    - bibtex / - project page / - code -

    -

    By integrating an edge-aware filter into a convolutional neural network we can learn an edge-detector while improving semantic segmentation.

    -
    -
    -
    - -
    - -
    - - Convolutional Color Constancy - -
    - Jonathan T. Barron -
    - ICCV, 2015 -
    - supplement / bibtex / video (or mp4) -

    -

    By framing white balance as a chroma localization task we can discriminatively learn a color constancy model that beats the state-of-the-art by 40%.

    -
    - - - - Scene Intrinsics and Depth from a Single Image - -
    - Evan Shelhamer, Jonathan T. Barron, Trevor Darrell -
    - ICCV Workshop, 2015 -
    - bibtex -

    -

    The monocular depth estimates produced by fully convolutional networks can be used to inform intrinsic image estimation.

    -
    - -
    - -
    - -
    - - Fast Bilateral-Space Stereo for Synthetic Defocus - -
    - Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernández -
    - CVPR, 2015   (Oral Presentation) -
    - abstract / - supplement / - bibtex / - talk / - keynote (or PDF) -

    -

    By embedding a stereo optimization problem in "bilateral-space" we can very quickly solve for an edge-aware depth map, letting us render beautiful depth-of-field effects.

    -

    This technology is used by the Google Camera "Lens Blur" feature.

    -
    - PontTuset - - - Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation - -
    - Jordi Pont-Tuset, Pablo Arbeláez, Jonathan T. Barron, Ferran Marqués, Jitendra Malik -
    - TPAMI, 2017 -
    - project page / - bibtex / - fast eigenvector code -

    -

    We produce state-of-the-art contours, regions and object candidates, and we compute normalized-cuts eigenvectors 20× faster.

    -

    This paper subsumes our CVPR 2014 paper.

    -
    -
    -
    - -
    - -
    - -
    -

    - - Shape, Illumination, and Reflectance from Shading - -
    - Jonathan T. Barron, Jitendra Malik -
    - TPAMI, 2015 -
    - bibtex / keynote (or powerpoint, PDF) / video / code & data / kudos -

    -

    - We present SIRFS, which can estimate shape, chromatic illumination, reflectance, and shading from a single image of an masked object. -

    -

    - This paper subsumes our CVPR 2011, CVPR 2012, and ECCV 2012 papers. -

    -
    - ArbalaezCVPR2014 - - - Multiscale Combinatorial Grouping - -
    - Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marqués, Jitendra Malik -
    - CVPR, 2014 -
    - project page / - bibtex -

    This paper is subsumed by our journal paper.

    -
    - -
    - -
    - -
    - - Volumetric Semantic Segmentation using Pyramid Context Features - -
    - Jonathan T. Barron, Pablo Arbeláez, Soile V. E. Keränen, Mark D. Biggin, -
    David W. Knowles, Jitendra Malik -
    - ICCV, 2013 -
    - supplement / - poster / - bibtex / video 1 (or mp4) / video 2 (or mp4) / code & data -

    - We present a technique for efficient per-voxel linear classification, which enables accurate and fast semantic segmentation of volumetric Drosophila imagery. -

    -
    - 3DSP - - - 3D Self-Portraits - -
    - Hao Li, Etienne Vouga, Anton Gudym, Linjie Luo, Jonathan T. Barron, Gleb Gusev -
    - SIGGRAPH Asia, 2013 -
    - video / shapify.me / bibtex -

    Our system allows users to create textured 3D models of themselves in arbitrary poses using only a single 3D sensor.

    -
    - -
    - -
    - - Intrinsic Scene Properties from a Single RGB-D Image - -
    - Jonathan T. Barron, Jitendra Malik -
    - CVPR, 2013   (Oral Presentation) -
    - supplement / bibtex / talk / keynote (or powerpoint, PDF) / code & data -

    By embedding mixtures of shapes & lights into a soft segmentation of an image, and by leveraging the output of the Kinect, we can extend SIRFS to scenes. -
    -
    TPAMI Journal version: version / bibtex -

    -
    - Boundary_png - - - Boundary Cues for 3D Object Shape Recovery - -
    - Kevin Karsch, - Zicheng Liao, - Jason Rock, - Jonathan T. Barron, - Derek Hoiem -
    - CVPR, 2013 -
    - supplement / bibtex -

    Boundary cues (like occlusions and folds) can be used for shape reconstruction, which improves object recognition for humans and computers.

    -
    - -
    - -
    - - Color Constancy, Intrinsic Images, and Shape Estimation - -
    - Jonathan T. Barron, Jitendra Malik -
    - ECCV, 2012 -
    - supplement / - bibtex / - poster / - video -

    This paper is subsumed by SIRFS.

    -
    -
    -
    - -
    - -
    - -
    - - Shape, Albedo, and Illumination from a Single Image of an Unknown Object - -
    - Jonathan T. Barron, Jitendra Malik -
    - CVPR, 2012 -
    - supplement / - bibtex / - poster -

    This paper is subsumed by SIRFS.

    -
    - b3do - - - A Category-Level 3-D Object Dataset: Putting the Kinect to Work - -
    - Allison Janoch, - Sergey Karayev, - Yangqing Jia, - Jonathan T. Barron, - Mario Fritz, - Kate Saenko, - Trevor Darrell -
    - ICCV 3DRR Workshop, 2011 -
    - bibtex / - "smoothing" code -

    We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.

    -
    - safs_small - - - High-Frequency Shape and Albedo from Shading using Natural Image Statistics - -
    - Jonathan T. Barron, Jitendra Malik -
    - CVPR, 2011 -
    - bibtex -

    This paper is subsumed by SIRFS.

    -
    - fast-texture - - - Discovering Efficiency in Coarse-To-Fine Texture Classification - -
    - Jonathan T. Barron, Jitendra Malik -
    - Technical Report, 2010 -
    - bibtex -

    A model and feature representation that allows for sub-linear coarse-to-fine semantic segmentation. -

    -
    @@ -3415,38 +71,7 @@
    - blind-date - - - Blind Date: Using Proper Motions to Determine the Ages of Historical Images - -
    - Jonathan T. Barron, David W. Hogg, Dustin Lang, Sam Roweis -
    - The Astronomical Journal, 136, 2008 -

    Using the relative motions of stars we can accurately estimate the date of origin of historical astronomical images.

    -
    - clean-usnob - - - Cleaning the USNO-B Catalog Through Automatic Detection of Optical Artifacts - -
    - Jonathan T. Barron, Christopher Stumm, David W. Hogg, Dustin Lang, Sam Roweis -
    - The Astronomical Journal, 135, 2008 -

    We use computer vision techniques to identify and remove diffraction spikes and reflection halos in the USNO-B Catalog.

    -

    In use at Astrometry.net

    -
    From 3894f7349179e6262a5b84ba63d4ce4a49ea86b8 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 17 Dec 2023 12:46:40 -0500 Subject: [PATCH 03/33] modified index --- index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index 9398722175..2554e5ad21 100755 --- a/index.html +++ b/index.html @@ -58,8 +58,8 @@ prl - - Parallelizing Reinforcement Learning + + Beyond Dynamic Programming
    Jonathan T. Barron, Dave Golland, Nicholas J. Hay From 36b79c7ff3914d15ee65e5021b8b7d61ca0cd584 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 17 Dec 2023 12:50:16 -0500 Subject: [PATCH 04/33] modified index --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 2554e5ad21..aa2528b8bf 100755 --- a/index.html +++ b/index.html @@ -62,7 +62,7 @@ Beyond Dynamic Programming
    - Jonathan T. Barron, Dave Golland, Nicholas J. Hay + Abhinav Muraleedharan
    Technical Report, 2009
    From e9bc33f4f3979639529634bdba2e09e93b3102f3 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 17 Dec 2023 12:56:28 -0500 Subject: [PATCH 05/33] modified bib file --- data/abhinav_bdp.bib | 6 ++++++ index.html | 6 +++--- 2 files changed, 9 insertions(+), 3 deletions(-) create mode 100644 data/abhinav_bdp.bib diff --git a/data/abhinav_bdp.bib b/data/abhinav_bdp.bib new file mode 100644 index 0000000000..fdc8b55e16 --- /dev/null +++ b/data/abhinav_bdp.bib @@ -0,0 +1,6 @@ +@article{muraleedharan2023beyond, + title={Beyond dynamic programming}, + author={Muraleedharan, Abhinav}, + journal={arXiv preprint arXiv:2306.15029}, + year={2023} +} \ No newline at end of file diff --git a/index.html b/index.html index aa2528b8bf..5ed809b272 100755 --- a/index.html +++ b/index.html @@ -64,10 +64,10 @@
    Abhinav Muraleedharan
    - Technical Report, 2009 + Research Paper (Under Review), 2023
    - bibtex -

    Markov Decision Problems which lie in a low-dimensional latent space can be decomposed, allowing modified RL algorithms to run orders of magnitude faster in parallel.

    + bibtex +

    In this paper, I introduced Score-life programming, a novel theoretical approach for solving reinforcement learning problems. In contrast with classical dynamic programming-based methods, the methods in this work can search over non-stationary policy functions, and can directly compute optimal infinite horizon action sequences from a given state.

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zt^{X))s%aZ5;Wb~!{ij%+L9#D?{IjedC!SFlu5FMFC-Z{q+Omh#om%SXm4z7mzi=} zTW5a~=%d~~>P|cOFcbU?5C91#1qihxIjC)qN3lAjAny0=W!|}x1+|0OqV|RU4`<#_ z{p=d6*+}czf9gItRm#p36m-WxoX$srB}h@z356V=+&HOkq1jEU3{WO z)BvhexB7u|W%YDgQ_YIjbYoSN3L|gt;urGgMdMtD+o1J75W4XS|GPH$6}A}T;~%5M zJ<=_-DI)AE!uvGqzzWov1h#k`nFPkqk3!l%GU7qTsnTyk9oU{-DFTP~s;*!hn(lFV z9D7NRL(DfaD+vv`` Abhinav Muraleedharan

    -

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof Nathan Wiebe. My research interests are reinforcement learning, quantum computing. +

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe. I'm interested in theoretical aspects of reinforcement learning and quantum computing.

    I invented Score-life programming.

    Email  /  - CV  /  + CV  /  Bio  /  Google Scholar  /  Twitter  /  @@ -44,7 +44,7 @@ Research

    - I'm interested in reinforcement learning and quantum algorithms. Much of my research is about developing novel reinforcement learning and quantum computing algorithms. + My reinforcement learning research focuses on the development of efficient reinforcement learning algorithms for training generally intelligent agents. I also work on developing efficient quantum algorithms for training large scale machine learning models.

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    Misc @@ -86,11 +86,11 @@ - - + From 02d2a6ca5b59badc36efffdabab62df9dcb1d754 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sat, 23 Dec 2023 22:41:28 +0530 Subject: [PATCH 08/33] made changes-final version --- data/JonBarron-bio.txt | 10 +--------- images/UofT.png | Bin 0 -> 4054 bytes index.html | 24 ++++++++++++------------ 3 files changed, 13 insertions(+), 21 deletions(-) create mode 100644 images/UofT.png diff --git a/data/JonBarron-bio.txt b/data/JonBarron-bio.txt index 1cfc8ed14f..1d3152fdc7 100644 --- a/data/JonBarron-bio.txt +++ b/data/JonBarron-bio.txt @@ -1,9 +1 @@ -Jon Barron is a senior staff research scientist at Google Research in San -Francisco, where he works on computer vision and machine learning. He received -a PhD in Computer Science from the University of California, Berkeley in 2013, -where he was advised by Jitendra Malik, and he received a Honours BSc in -Computer Science from the University of Toronto in 2007. He received a National -Science Foundation Graduate Research Fellowship in 2009, the C.V. Ramamoorthy -Distinguished Research Award in 2013, and the PAMI Young Researcher Award in -2020. His works have received awards at ECCV 2016, TPAMI 2016, ECCV 2020, ICCV -2021, CVPR 2022, the 2022 Communications of the ACM, and ICLR 2023. \ No newline at end of file +I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe. 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    (Philosophical)
    From 5a240275383b487fb590f6e3b6f3f6c7f26e31ec Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 22:56:09 +0530 Subject: [PATCH 09/33] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..1a0f8574de --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +abhinavmuraleedharan.com \ No newline at end of file From 851bab068abd8402e3d04e388acc38d6259cbe90 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:15:20 +0530 Subject: [PATCH 10/33] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 1a0f8574de..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -abhinavmuraleedharan.com \ No newline at end of file From d66202ea99c4d1d8dd04188b41db689c6a8d474b Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:15:44 +0530 Subject: [PATCH 11/33] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..1a0f8574de --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +abhinavmuraleedharan.com \ No newline at end of file From daf241c3aaa32bb0045bb3f8fd6f84806a162676 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:22:33 +0530 Subject: [PATCH 12/33] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 1a0f8574de..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -abhinavmuraleedharan.com \ No newline at end of file From 685ab085c18083a49e0fbe64e12c083629afd90f Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:23:55 +0530 Subject: [PATCH 13/33] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..1a0f8574de --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +abhinavmuraleedharan.com \ No newline at end of file From 9988636c5388b4d127f144b87daf3b5c19bf495e Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:27:55 +0530 Subject: [PATCH 14/33] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 1a0f8574de..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -abhinavmuraleedharan.com \ No newline at end of file From dca6b95baaf88ea77999c5f710ac91b0f43e2f3d Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 23 Dec 2023 23:30:02 +0530 Subject: [PATCH 15/33] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..1a0f8574de --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +abhinavmuraleedharan.com \ No newline at end of file From 8fd2ee014aad4f3c861068e7716370fd784f5a7b Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 29 Jan 2024 11:26:10 +0530 Subject: [PATCH 16/33] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 1a0f8574de..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -abhinavmuraleedharan.com \ No newline at end of file From e2227803a45334618af7a60e11f1569951a97e32 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 29 Jan 2024 11:27:14 +0530 Subject: [PATCH 17/33] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..1a0f8574de --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +abhinavmuraleedharan.com \ No newline at end of file From 0d0b32e7b551b380100bf7bce0fc078ae7d6d506 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 12 Feb 2024 15:19:18 -0500 Subject: [PATCH 18/33] Add files via upload --- data/thesis.pdf | Bin 0 -> 1368404 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 data/thesis.pdf diff --git a/data/thesis.pdf b/data/thesis.pdf new file mode 100644 index 0000000000000000000000000000000000000000..df56c8e9e0d0dfeaece4e7175c4cb00476fd6d9a GIT binary patch literal 1368404 zcmb5VQD z)gM8*m=g3D5J3!qoZxsZ6Y7uZ9(Pxi;Q*tyrwUk|tbW)1#pIEZC|uo_)y*=Sgx(Wj z#^XH5jcz{%uEM}Y)ob*o04zz593gLnOP@7j@owxn|D-%Y18^g80}O&$1%97Py!Y#F zcq?GR^pfE21DV7csLgcQQ_md0H?07s(28J49D0mQnLhhe5G*-NL*NtcNl&*A$|(k4 z=gVOur~md(5r#>NNvlJ1_m=Ykd$39y>ab?jdXCr6)6 zA&?~dYqTYCRt2czTRBdj)D4&voSi>Ae##GLfuUwT_IoVCILy$tE(1Jon$2QtHe(JI zaZ=Lj6h2RzV@k=V1_zahD&$jt3<&W>jXdR)M9b`)t&-12>Lbpz*%SaTUlg|BYirJ_ zNVU}J>PO{0I6`t%craW_JT9sWW*)nb;ctZ>s$B z_djXI$oil8#>mFW{@>SB$pSt0`^UFyQm-iF}XO*sM|RGOG6k7M~Oue$sEeueW=v*W1Bn zr6dW0bVbs(5|MHWYP;M-gINvS<=yO_Hno+hHBgx&9;gQdvdP=@=J@6uJgvCfVrG;9woPT?d}p zwP-G@2XWy`$QTr5qGDyWh5F|!n>zm18GBqK;`z>0OHqZ!0EdmUCHy`2?9F3od_~E& z!PS*R={xdWH&WaTzWMnzQwH&LC%PTj>df1V2t-1PB3YcGag4I{sLyTJdO6d#3=w}p z^Isi1vcsb63Fi`Zsl+9DQ#e;cQJmfbY(M**4M^1T{JHK zPqb4Qh-@M;DB6WN=3QH*RmwNhSXD$5E`NoC*#5HSZs-8`-D?*7SlIu;ZPo!udU z0>ZY+%w$&k@NI|)CaRdZV51WO-$iRd0@0Fq09}%aak*l(6u6PrE?h#O&OCN0pgQcd z3PH<}0^D{p`F}w`Em*i)l8nA{-7F-BXM-J*F{1*4$%LUBCXq#_+Za&{Ek)q&{^zmKJ6I_o4~Ts$#q`)J z`co#(bGO~xnj7Rk299D<9%4|rKg}H-?XruNftCu)B7-rIEc!d-Sic}Fo~^kIoqjZW<~*8ZwF^6i!y5Zq zM^qXn9)yy5;BFO-GFpeih8NoB%CFw|C1O+D#~UyxFPxGxjmXoO98RnjWa)9o{j- z+LnPx@%*%4UHID4B?2e;v17`RDM(g;m*`EEXlftdjZ)##5oD<+*-}$^?K5h%2aO|V zH1xCD{CQP>Qdm{+FcHl4;ock@JRh0POhl4+dK&vf4&-E!FGf}xRWr<~#K4P^iaG*u zZr}^_4BSsKt2{{3MTLiZxE@Y_l|+OLmqU6|Bk9|q0-L0$xj50UJ;CpS_qKKK1NM;Q zl7Xtyhb^ZJ{fo~}wl=4fDx5!6DGXv$NSzT-!+xA@H5p=%8ZP?AbF7FjLJs0LvbwoD z_6$h?wP4`Zo>35hq!-06BhGk|4F3Qc_?)GlorVwBpk;P+N>!36W$wS@Yz7>{UzG)qX{N}+R1k`Yc z$!>Om8X$bCzUwKy1l(vm&fA@hO|R{3=P`sA zx9l!pu2mMl^_27%^XoK>?PMP;O3FiJbx#f2*F1^o=dTi}y&WC^|JE;1>ly{Ye-a)O z)4vcN3lqox55kMvWJBmVReM8&Tr+t?puAs0lbi>#iR=@FmxXT}(qX2SjwLl~`t4!H zk!opn^+wVY_I>;?9a4*Qii<@2nGFRjnlQl_#(j&9SXgUP8IAdx2e_IG6IXcFRC3+$$S3J739n}oKQQ@-OQ z96ebUr9Ix3gx&dR8^=d!bnyCb`J4ydThzfT7cylMPsa+bKdruOtQ8@@1qKbc^-9NK zLA2GgwAPJx2x9bNyrVmrh#SXVW$jD;C#$+r z>>x+6{fUbThV^om2X?tJo|iz6ktAbfj(`ntx51g>pYQ1VwDiV-Mr{+P0X1!rTzS$z z?RB^{KD-|nlzSoLTLNuN%+PfC8UwE)m1Gn!p1W}$1AJXx%jOy58ia-jVpRer?(j5Q zC6VOx#Xk3%hxHs4AWXHxmMHA8ygtAqhEfdTO&G1K3?`YVqA;8=mk5MN&tE{nbGSV7 z0Wi8`sVuANihzRX?_V>~T24_q4T@Tn52_zQ7>K zV5K&UY1lk0!a095S+sS0z#(Pr&xfsmU}EKJwQeWd)myM|FA_u)a}-4d~;u zn^_Hy6Y|w^J}M-8cW`2s{4PrGNHk#tV~q`R;BteZj0nAi2eT@Ut>)CXvsg)8G9!toXh!H4Zl6t5>^6QK2Q83RV7PjKKC>`YT?eg6$*D4vA)!8^$F7z=^gJ;& z!%XigfCa?FgvaQM&Sa^W6!MmIBes008f5zm8Oh!6sWa&flVw8u4+&E+4Rc!xS`Qlm z_E(0;LO1<07F5~mSNzlx3y!z=TE7`iko+)%tUt;f05_44$}79|lk^4AS9@$UdLy|P zO!M5;ZLU#f7aU%FM1k;vajG5^LUVCd*`sXWa!)Gj$=JKWa=7T_w~R{A@@J`Y$$*2S ze&Y?_BFjVTdGIFaG$Db~!iAg;QXCj@xERxCdEj8!640={tWdrrn_NQ!PlMdPL0Po% zVJv4Rpvgu7bvGr9d!CZAZLdFtcc0H$zkmqq=W73g@&6*C8JU^>YY}ZT#S1+k0OP;k z-Ai##88mPvF~nYp0d07()zREtRbjCI`)Z|q+bB! zk_BO;ezQ8d>uj_P9oR*V@Sk`3=lK887+_>&V*YP=%SCNG@gLsmzNB_%R%bPY2)tyR zTr=U;Ra7Mf9|aE&7Mc1(A{`)JXus2P`_t!xg?Jg

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    - Demo Chair, CVPR 2023 + + Finalist, Indian Innovation Challenge 2017
    - Area Chair, CVPR 2022 + , CVPR 2022
    Area Chair & Longuet-Higgins Award Committee Member, CVPR 2021
    @@ -115,7 +115,7 @@
    - Basically
    Blog Posts
    + Blog Posts
    (Philosophical)
    Squareplus: A Softplus-Like Algebraic Rectifier @@ -133,7 +133,7 @@

    - Feel free to steal this website's source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page. + This website code is borrowed from: source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page.

    Finalist, Indian Innovation Challenge 2017
    - , CVPR 2022 + Awardee,Kerala Overseas Education Scholarship
    - Area Chair & Longuet-Higgins Award Committee Member, CVPR 2021 + Spot Award, Collins Aerospace
    - Area Chair, CVPR 2019 +
    - Area Chair, CVPR 2018 +
    - cs188 + cs188 - Graduate Student Instructor, CS188 Spring 2011 + + Course Instructor, UofT Engineering Outreach Office, DEEP Summer Academy
    - Graduate Student Instructor, CS188 Fall 2010 + Course Instructor,Math Outreach Office, UofT
    - Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition +
    - Squareplus: A Softplus-Like Algebraic Rectifier + Love and other things
    - A Convenient Generalization of Schlick's Bias and Gain Functions + The Measuring Instrument
    - Continuously Differentiable Exponential Linear Units +
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<39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 12 Feb 2024 15:20:35 -0500 Subject: [PATCH 19/33] Update index.html --- index.html | 1 + 1 file changed, 1 insertion(+) diff --git a/index.html b/index.html index 789592dcde..afc6dbab59 100755 --- a/index.html +++ b/index.html @@ -32,6 +32,7 @@ Google Scholar  /  Twitter  /  Github + MS Thesis

    From b52d0fd5d74f64f879a937c11c4d9d1558788f13 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 12 Feb 2024 15:21:33 -0500 Subject: [PATCH 20/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index afc6dbab59..7dcc9c678b 100755 --- a/index.html +++ b/index.html @@ -32,7 +32,7 @@ Google Scholar  /  Twitter  /  Github - MS Thesis + MS Thesis

    From a599f8ccb254ca1bc6f433aa86b48843974ac6cf Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Mon, 12 Feb 2024 15:22:25 -0500 Subject: [PATCH 21/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 7dcc9c678b..fd2513c08f 100755 --- a/index.html +++ b/index.html @@ -31,7 +31,7 @@ Bio  /  Google Scholar  /  Twitter  /  - Github + Github  /  MS Thesis

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zi;pfU1ZW=_DInAju#RN9$ZtVCGG6=?pcmnO<%>F18@2x>~Gx$TTLw+o^ zMo=vmssLoiKgT@!QI=o>c5Dma$_@P_v@%JnkAr$EH>7pXXybHfi3lI-IYEp^l^_u;m z)9)oBw7WiyzQ`>7KBO^#TT+Ysze6GM-+zBw;BO23ZGpcn@V5p2w!q&O_}c=1Ti|aC j{B421E%5)s0>FeVBWG7nXESp+Ru&F83JNg=ak&2jYWzg; literal 0 HcmV?d00001 From db5034a1cc02048527dc0dd7ab139263df0220af Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sat, 6 Apr 2024 23:17:40 -0400 Subject: [PATCH 23/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index fd2513c08f..f8759e868a 100755 --- a/index.html +++ b/index.html @@ -32,7 +32,7 @@ Google Scholar  /  Twitter  /  Github  /  - MS Thesis + MS Thesis

    From 3cc69082111b37dc1445e0e4954abd7ca87ea85f Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:32:51 -0400 Subject: [PATCH 24/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index f8759e868a..700f13acfa 100755 --- a/index.html +++ b/index.html @@ -20,7 +20,7 @@

    Abhinav Muraleedharan

    -

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe. I'm interested in theoretical aspects of reinforcement learning and quantum computing. +

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe and Prof. Roger Grosse. I'm interested in theoretical aspects of reinforcement learning and quantum computing.

    I invented Score-life programming. From b40a646a676b1947a69ff436f27ced5b9ff5c668 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:35:14 -0400 Subject: [PATCH 25/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 700f13acfa..291cf36c70 100755 --- a/index.html +++ b/index.html @@ -20,7 +20,7 @@

    Abhinav Muraleedharan

    -

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe and Prof. Roger Grosse. I'm interested in theoretical aspects of reinforcement learning and quantum computing. +

    I am a graduate student at the University of Toronto. At UofT, I work under the supervision of Prof. Nathan Wiebe and Prof. Roger Grosse. My research interests span Quantum Algorithms, Reinforcment Learning, and Alignment of large language models.

    I invented Score-life programming. From cd747a77b53a90c315f2e7024b87cf3d79ea895d Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 30 Jun 2024 13:47:52 -0400 Subject: [PATCH 26/33] added formal analysis of lige blog post --- formal_analysis_of_life.html | 120 +++++++++++++++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 formal_analysis_of_life.html diff --git a/formal_analysis_of_life.html b/formal_analysis_of_life.html new file mode 100644 index 0000000000..60a7c39655 --- /dev/null +++ b/formal_analysis_of_life.html @@ -0,0 +1,120 @@ + + + + + + Formal Analysis of Life + + + +

    +

    Formal Analysis of Life

    +

    By Abhinav Muraleedharan

    +

    +
    + +
    +
    +

    Death

    +

    Any discussion about life should start with death. Death is the singular event that adds infinite value to every single second of human life. Hence, the most important task in any person's life is to figure out how to optimally allocate this resource of infinite value (time).

    +

    On a day to day basis, in our professional lives, we deal with questions like: What projects should we work on? Who should we spend time with? What books should I spend time reading? Which job/company should I choose?

    +

    There are two ways to answer these questions. The first way, as many people do, is to compute the return on any selected choices and choose the one with the maximum expected cumulative return. In the context of selecting projects or jobs, this would mean one should choose one that provides the highest return/salary.

    +

    This line of thinking however ignores the first point which we mentioned. Every second of our life is of infinite value. However, the return on any project is finite. Should we waste time by spending it on projects that have limited significance? Or should you work on the grand open challenges in science or at the frontiers of technology? If not monetary value, how to make decisions that might look sub-optimal in the short term, but optimal in the long term? The big shift here is that we consider time as the only currency we have, and see life decisions as investments of time, in some form.

    +
    + +
    +

    Defining 'Purpose'

    +

    Mathematically, physical systems can be seen as optimization engines, minimizing some functional over time. For instance, the dynamics of a pendulum is defined by the minimization of the Lagrangian, \( L = \int ( T(t) -V(t) )dt \). Your brain is a physical system, and you can view it as an optimization engine. In the case of the brain, assuming some notion of free will, we will have some access to choosing the form of the Lagrangian functional.

    +
    + +
    +

    The Optimization Problem

    +

    Pure Impact

    +
    J =  \int_{t=0}^{T} I(t)
    +

    Pure Understanding

    +
    J =  \int_{t=0}^{T}  U(t)
    +

    Understanding + Impact

    +
    J =  \int_{t=0}^{T} I(t) + U(t)
    +
    + +
    +

    Power

    +

    After choosing the functional, now comes the difficult part. Computing actions that would minimize the functional over a long time horizon. This step is difficult, because for instance, say if your goal is to understand the universe, then your actions would involve proof steps to solve the open problems in theoretical physics, or in other words- solving the open problems. If your choice is to maximize impact, then...

    +

    The word 'Power' has a bad connotation to it. People think power is evil, and often mistrusts people of power. Power simply means how much control you have over the state of the world. Can you drive the state of the world to the state you desire? Power can be roughly categorized into three.

    + +

    A Rough Classification

    +

    Political Power

    +

    Political power simply means how much you can influence the behavior of another person by communication. If you're charismatic, a great speaker, then you'll have a high degree of power over other people.

    +

    Intellectual Power

    +

    Intellectual power is proportional to how deeply you can think about a topic without losing attention.

    +

    Economic Power

    +

    Economic Power is proportional to the amount of money you have in the bank.

    + +

    Exponential Ascent and Exponential Descent

    +

    The most important thing to keep in mind regarding power is that there are only two trajectories of power. As you acquire more power, you have a much better chance of growing power. Either you acquire power exponentially, or your power decays exponentially into death.

    +
    + +
    +

    Qualities

    +

    Attention (Focus)

    +

    ...

    +

    Perseverance

    +

    ...

    +
    + +
    +

    Birth

    +

    This article began with death. Although all we have is finite time in this universe of infinite complexity, having been born as a sentient being is a thing by itself. What's the probability of me writing this article and you reading this? Close to zero. The ultimate gift in one's life is life itself. Being alive, pondering the big questions, is a gift. Go and make every second count.

    +
    + +
    +

    Acknowledgements

    +

    ...

    +
    +
    + +
    +

    © 2024 Abhinav Muraleedharan. All rights reserved.

    +
    + + From 932d6245d59d1228c54b78243ac0a1bff1a97842 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 30 Jun 2024 13:52:45 -0400 Subject: [PATCH 27/33] modified formal analysis of life --- formal_analysis_of_life.html | 31 +++++++++++++------------------ 1 file changed, 13 insertions(+), 18 deletions(-) diff --git a/formal_analysis_of_life.html b/formal_analysis_of_life.html index 60a7c39655..f47fc4ddfc 100644 --- a/formal_analysis_of_life.html +++ b/formal_analysis_of_life.html @@ -19,32 +19,27 @@ padding: 10px 0; text-align: center; } + header { + margin-bottom: 20px; + } article { background: #fff; padding: 20px; - margin: 20px 0; + margin: 0 auto; + margin-bottom: 20px; border-radius: 8px; - box-shadow: 0 0 10px rgba(0, 0, 0, 0.1) + box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + max-width: 800px; } h1, h2, h3, h4 { - color: #111331; + color: #333; } p { margin: 1em 0; } - pre { - background: #f4f4f4; - border: 1px solid #ddd; - padding: 10px; - overflow: auto; - } - code { - font-family: "Courier New", Courier, monospace; - background: #f4f4f4; - padding: 2px 4px; - border-radius: 4px; - } + +
    @@ -70,11 +65,11 @@

    Defining 'Purpose'

    The Optimization Problem

    Pure Impact

    -
    J =  \int_{t=0}^{T} I(t)
    +

    \[ J = \int_{t=0}^{T} I(t) \]

    Pure Understanding

    -
    J =  \int_{t=0}^{T}  U(t)
    +

    \[ J = \int_{t=0}^{T} U(t) \]

    Understanding + Impact

    -
    J =  \int_{t=0}^{T} I(t) + U(t)
    +

    \[ J = \int_{t=0}^{T} I(t) + U(t) \]

    From 83af350fa3ab92c1fa3430980959907304b6c45b Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 30 Jun 2024 14:00:06 -0400 Subject: [PATCH 28/33] adjusted colors --- formal_analysis_of_life.html | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/formal_analysis_of_life.html b/formal_analysis_of_life.html index f47fc4ddfc..30cc00754c 100644 --- a/formal_analysis_of_life.html +++ b/formal_analysis_of_life.html @@ -10,29 +10,32 @@ line-height: 1.6; margin: 0; padding: 20px; - background-color: #f4f4f4; - color: #333; + background-color: #f6ebeb; + color: #000000; } header, footer { - background: #333; - color: #fff; - padding: 10px 0; + background: #fbf6f6; + color: #090808; + padding: 5px 0; text-align: center; } header { margin-bottom: 20px; } article { - background: #fff; + background: #e8dfdf; padding: 20px; margin: 0 auto; margin-bottom: 20px; border-radius: 8px; - box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + box-shadow: 0 0 10px rgba(142, 134, 134, 0.1); max-width: 800px; } h1, h2, h3, h4 { - color: #333; + color: #020202; + } + h2, h3, h4 { + color: #141313; } p { margin: 1em 0; From 0069d8a40df3857d1de5f8fc65c1cbb7df9e44bb Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan Date: Sun, 30 Jun 2024 14:11:52 -0400 Subject: [PATCH 29/33] modified formal analysis html code --- formal_analysis_of_life.html | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/formal_analysis_of_life.html b/formal_analysis_of_life.html index 30cc00754c..a30efc4f91 100644 --- a/formal_analysis_of_life.html +++ b/formal_analysis_of_life.html @@ -10,17 +10,17 @@ line-height: 1.6; margin: 0; padding: 20px; - background-color: #f6ebeb; + background-color: #e2d1d1; color: #000000; } header, footer { - background: #fbf6f6; + background: #e2d1d1; color: #090808; padding: 5px 0; text-align: center; } header { - margin-bottom: 20px; + margin-bottom: 10px; } article { background: #e8dfdf; @@ -33,6 +33,8 @@ } h1, h2, h3, h4 { color: #020202; + font-family: 'Times New Roman', Times, serif; + font-size: xx-large; } h2, h3, h4 { color: #141313; @@ -47,7 +49,7 @@

    Formal Analysis of Life

    -

    By Abhinav Muraleedharan

    +

    Abhinav Muraleedharan

    From 18aeb6f0b13869e46bec77ea2d376c636af7a973 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Thu, 18 Jul 2024 21:34:24 -0400 Subject: [PATCH 30/33] Update index.html --- index.html | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/index.html b/index.html index 291cf36c70..57d8bf7d10 100755 --- a/index.html +++ b/index.html @@ -90,10 +90,8 @@
    Finalist, Indian Innovation Challenge 2017 -
    - Awardee,Kerala Overseas Education Scholarship +

    - Spot Award, Collins Aerospace

    From 5a6885373912812c92a4ee62778a10b386d06274 Mon Sep 17 00:00:00 2001 From: Abhinav Muraleedharan <39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Tue, 3 Sep 2024 10:05:30 -0400 Subject: [PATCH 31/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 57d8bf7d10..530a3c2876 100755 --- a/index.html +++ b/index.html @@ -117,7 +117,7 @@ Blog Posts
    (Philosophical)
    - Love and other things + NA
    The Measuring Instrument
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<39185275+Abhinav-Muraleedharan@users.noreply.github.com> Date: Thu, 5 Sep 2024 22:35:57 -0400 Subject: [PATCH 33/33] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 530a3c2876..d198bfe65e 100755 --- a/index.html +++ b/index.html @@ -27,7 +27,7 @@

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