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[UAI 2024] Revisiting Kernel Attention with Correlated Gaussian Process Representation

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CGPT_code_submission

Code submission for "Revisiting Kernel Attention via Correlated Gaussian Process Representation"

Requirements

We follow the previously published implementation of the paper Calibrating Transformers via Sparse Gaussian Processes. The setup for Anaconda environment is as follows:

  1. Create new python environment with version python=3.8 and activate it
  2. Run pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
  3. Run pip install -r requirements.txt in this directory to install the remaining packages.
  4. Download the CIFAR10 dataset here to data.
  5. Download the COLA dataset here to data.

Image classification on CIFAR10

The code for this task is in CIFAR10. Please refer to this README.md file for more details.

Linguistic acceptability on COLA

The code for this task is in COLA. Please refer to this README.md file for more details.

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[UAI 2024] Revisiting Kernel Attention with Correlated Gaussian Process Representation

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