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research.yaml
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---
- title: Text Style Transfer
description: "The NLP task of text style transfer (TST) aims to automatically control
the style attributes of a piece of text while preserving the content, which is an
important consideration for making NLP more user-centric. In this report, we explore
text style transfer through an applied use case — neutralizing subjectivity bias in
free text. Along the way, we describe our sequence-to-sequence modeling approach
leveraging HuggingFace Transformers, and present a set of custom, reference-free
evaluation metrics for quantifying model performance. Finally, we conclude with a
discussion of ethics centered around our prototype: Exploring Intelligent Writing Assistance."
category: Research Report
tags:
- cml
- ffl
- ml
- nlp
link: https://text-style-transfer.fastforwardlabs.com/
imgpath: https://text-style-transfer.fastforwardlabs.com/figures/FF24_cover.png
date: '2021-09-15T00:00:00Z'
- title: Inferring Concept Drift Without Labeled Data
description: Concept drift occurs when the statistical properties of a target domain
change over time causing model performance to degrade. Drift detection is generally
achieved by monitoring a performance metric of interest and triggering a retraining
pipeline when that metric falls below some designated threshold. However, this
approach assumes ample labeled data is available at prediction time - an unrealistic
constraint for many production systems. In this report, we explore various approaches
for dealing with concept drift when labeled data is not readily accessible.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://concept-drift.fastforwardlabs.com/
imgpath: https://concept-drift.fastforwardlabs.com/figures/FF22-cover.png
date: '2021-08-01T00:00:00Z'
- title: Exploring Multi-Objective Hyperparameter Optimization
description: We develop machine learning models against the “usual suspect” metrics
like predictive accuracy, recall, and precision. However, these metrics are rarely
truly all we care about. Production models must also satisfy physical requirements
such as latency or memory footprint, or fairness constraints. Hyperparameter optimization
becomes even more challenging when we have multiple metrics to optimize. Our latest
research examines this “multi-objective” hyperparameter optimization scenario
in detail.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://blog.fastforwardlabs.com/2021/07/07/exploring-multi-objective-hyperparameter-optimization.html
imgpath: https://www.cloudera.com/content/dam/www/marketing/images/promos/ff21-combo.png
date: '2021-07-07T00:00:00Z'
- title: Deep Learning for Automatic Offline Signature Verification
description: Handwritten signature verification aims to automatically discriminate
between genuine and forged signatures, and is a particularly important challenge
due to the ubiquity of handwritten signatures as a form of identification in legal,
financial, and administrative domains. This research cycle explored the use of
deep metric learning approaches - specifically siamese networks - combined with
novel feature extraction methods to improve upon traditional techniques.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://blog.fastforwardlabs.com/2021/05/26/deep-learning-for-automatic-offline-signature-verification-an-introduction.html
imgpath: https://blog.fastforwardlabs.com/images/hugo/metricblog/signature_pipeline.png
date: '2021-05-26T00:00:00Z'
- title: Session-based Recommender Systems
description: Recommendation systems have become a cornerstone of modern life, spanning
sectors that include online retail, music and video streaming, and even content
publishing. These systems help us navigate the sheer volume of content on the
internet, allowing us to discover what’s interesting or important to us. A key
trend over the past few years has been session-based recommendation algorithms
that provide recommendations solely based on a user’s interactions in an ongoing
session, and which do not require the existence of user profiles or their entire
historical preferences.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://session-based-recommenders.fastforwardlabs.com/
imgpath: https://session-based-recommenders.fastforwardlabs.com/figures/ff19_cover_splash.png
date: '2021-05-01T00:00:00Z'
- title: Few-Shot Text Classification
description: Text classification can be used for sentiment analysis, topic assignment,
document identification, article recommendation, and more. While dozens of techniques
now exist for this fundamental task, many of them require massive amounts of labeled
data in order to be useful. Collecting annotations for your use case is typically
one of the most costly parts of any machine learning application. In this report,
we explore how latent text embeddings can be used with few (or even zero) training
examples and provide insights into best practices for implementing this method.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://few-shot-text-classification.fastforwardlabs.com/
imgpath: https://few-shot-text-classification.fastforwardlabs.com/figures/ff18-cover-splash.png
date: '2020-12-01T00:00:00Z'
- title: Structural Time Series
description: Time series data is ubiquitous. This report examines generalized additive
models, which give us a simple, flexible, and interpretable means for modeling
time series by decomposing them into structural components. We look at the benefits
and trade-offs of taking a curve-fitting approach to time series, and demonstrate
its use via Facebook’s Prophet library on a demand forecasting problem.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://structural-time-series.fastforwardlabs.com/
imgpath: https://structural-time-series.fastforwardlabs.com/figures/ff16-cover-splash.png
date: '2020-10-31T00:00:00Z'
- title: Meta Learning
description: In contrast to how humans learn, deep learning algorithms need vast
amounts of data and compute and may yet struggle to generalize. Humans are successful
in adapting quickly because they leverage their knowledge acquired from prior
experience when faced with new problems. In this report, we explain how meta-learning
can leverage previous knowledge acquired from data to solve novel tasks quickly
and more efficiently during test time
category: Research Report
tags:
- cml
- ffl
- ml
link: https://meta-learning.fastforwardlabs.com/
imgpath: https://meta-learning.fastforwardlabs.com/figures/ff15-cover-splash.png
date: '2020-09-30T00:00:00Z'
- title: Automated Question Answering
description: Automated question answering is a user-friendly way to extract information
from data using natural language. Thanks to recent advances in natural language
processing, question answering capabilities from unstructured text data have grown
rapidly. This blog series offers a walk-through detailing the technical and practical
aspects of building an end-to-end question answering system.
category: Research Report
tags:
- cml
- ffl
- ml
link: https://qa.fastforwardlabs.com/
imgpath: https://www.cloudera.com/content/dam/www/marketing/images/cffl/ff14-combo.png
date: '2021-07-22T00:00:00Z'