OATML / non-parametric-transformers
Code for "Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning"
☆404Updated 8 months ago
Related projects ⓘ
Alternatives and complementary repositories for non-parametric-transformers
- ☆466Updated 3 months ago
- ☆295Updated 5 months ago
- The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive …☆404Updated 2 years ago
- ☆235Updated last year
- Cockpit: A Practical Debugging Tool for Training Deep Neural Networks☆473Updated 2 years ago
- Hopular: Modern Hopfield Networks for Tabular Data☆306Updated 2 years ago
- BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.☆562Updated this week
- An alternative to convolution in neural networks☆251Updated 7 months ago
- A library to inspect and extract intermediate layers of PyTorch models.☆470Updated 2 years ago
- This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a…☆302Updated this week
- Laplace approximations for Deep Learning.☆471Updated this week
- Implementation of Estimating Training Data Influence by Tracing Gradient Descent (NeurIPS 2020)☆219Updated 2 years ago
- ☆366Updated last year
- ☆187Updated 2 years ago
- Fast Differentiable Sorting and Ranking☆574Updated 9 months ago
- Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions☆258Updated last year
- Fast, differentiable sorting and ranking in PyTorch☆774Updated 10 months ago
- Optimal Transport Dataset Distance☆156Updated 2 years ago
- Enabling easy statistical significance testing for deep neural networks.☆330Updated 4 months ago
- MADGRAD Optimization Method☆803Updated last year
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆187Updated 3 weeks ago
- Python implementation of GLN in different frameworks☆95Updated 4 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆111Updated 2 years ago
- Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"☆417Updated 2 years ago
- Implementation of ETSformer, state of the art time-series Transformer, in Pytorch☆149Updated last year
- Long Range Arena for Benchmarking Efficient Transformers☆731Updated 11 months ago
- A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.☆552Updated 9 months ago
- ☆191Updated 3 years ago
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020☆235Updated last year