Awesome

LANTK

This is the code repository for the NeurIPS paper Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity. If you use this code for your work, please cite

@article{chen2020label,
  title={Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity},
  author={Chen, Shuxiao and He, Hangfeng and Su, Weijie J},
  journal={NeurIPS},
  year={2020}
}

Installing Dependencies

Use virtual environment tools (e.g miniconda) to install packages and run experiments
python>=3.6
pip install -r requirements.txt

Code Organization

The code is organized as follows:

• CNN.py (CNN for binary classification)
• ntk_google.py (CNTK for binary classification)
• ntl_google.py (LANTK-HR for binary classification)
• CNN_multi.py (CNN for multi-class classification)
• ntk_google_multi.py (CNTK for multi-class classification)
• ntl_google_multi.py (LANTK-HR for multi-class classification)
• nn.py (2-layer NNs)
• ntk.py (NTK for 2-layer NNs)
• ntl.py (LANTK-HR for 2-layer NNs)
• ntk_approx.py (Approximate NTK $K_t^{(2)}$)
• ntk_simple_accelerate.py (LANTK-NTH)
• MSCOCO/label_dynamic.py (Dynamics of local elasticity over training)

Reproducing experiments

To reproduce the experiments for CNN/CNTK/LANTK-HR on binary classification:

sh run_cnn_experiments.sh
sh run_ntk_google_experiments.sh
sh run_ntl_google_experiments.sh

Note that these commands are similar for CNN/CNTK/LANTK-HR on multi-class classification and NN/NTK/LANTK-HR for 2-layer NNs.

To reproduce the experiments for LANTK-NTH:

python ntl_simple_accelerate.py neg=3 pos=5 (an example)

To reproduce the experiments for dynamics of local elasticity over training:

CUDA_VISIBLE_DEVICES=1 python MSCOCO/labels_dynamic.py dataset=MSCOCO method_option=kernel 
model_option=MLPNet loss_option=MSE pos_one=dog pos_two=bench neg_one=cat neg_two=chair label_system=one