Randomized Geometric Algebra Methods for Convex Neural Networks

Video demonstration

The videos which demonstrate the entire path of decision region of the (subsampled) Convex Lasso method with respect to the regularization parameter $\beta$ are available at here.

Numerical experiments

Download datasets

• Download IMDB dataset from here and Amazon dataset from here and store them under the folder ./data/.
• Download GLUE-QQ, GLUE-COLA, MNIST and CIFAR10 datasets based on the notebook dl_dataset_huggingface.ipynb.
• The ECG signals are stored in the WFDB format at a sampling rate of 100Hz. The signals are unpacked using the python package -https://wfdb.readthedocs.io/en/latest/. Additionally, text features are extracted using OpenAI’s embedding model.

Install packages

pip install -r requirements.txt

Preprocess data

Run the following lines to obtain OpenAI embedding data.

python3 preprocess-dataset.py --data_path ./data --export_num full --embedding OpenAI --data_name IMDB

python3 preprocess-dataset.py --data_path ./data --export_num 30K --embedding OpenAI --data_name Amazon

python3 preprocess-dataset.py --data_path ./data --export_num 50K --embedding OpenAI --data_name glue-qqp

python3 preprocess-dataset.py --data_path ./data --export_num full --embedding OpenAI --data_name glue-cola

Open the notebook ecg_signal_extraction_from_wfdb.ipynb.

Reproduce results

For the result on 2D spiral dataset, simply open the notebook Illustration_spiral.ipynb.

For the results on Feature-based transfer learning, run the following lines:

for data in IMDB Amazon cola qqp ECG-signal ECG-report mnist cifar10
do
    for tm in cvx noncvx
    do
            python3 main_FT_input_num.py --data_path ./data/ --data_name $data --seed 1 --train_method $tm --Epochs 20 --train_choice f1 --embed OpenAI  --Hidden 50 --train_num f1 --shuffle --add_eps --aug_sym        
    done
done

Plot figures

Open the notebook FT_plot_input_num.ipynb