YannDubs/Neural-Process-Family
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Trying to train useful models
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Experiments for understanding disentanglement in VAE latent representations
Hydra is a framework for elegantly configuring complex applications
Minimum viable code for the Decodable Information Bottleneck paper. Pytorch Implementation.
Generic image compressor for machine learning. Pytorch code for our paper "Lossy compression for lossless prediction".
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"
PyTorch implementation of Hash Embeddings (NIPS 2017). Submission to the NIPS Implementation Challenge.
Benchmark and analysis of 165 pretrained SSL models. Code for "Evaluating Self-Supervised Learning via Risk Decomposition".
Analyze single-cell miRNA sequencing data from HL-60 cells along a 7-day time-course of ATRA treatment.
links and status of cool gradio demos
Toolbox of models, callbacks, and datasets for AI/ML researchers.
Submission to https://pytorch.org/hub/
A scikit-learn compatible neural network library that wraps PyTorch
Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch
Experiments for comparing and understanding different neural processes architectures.
Novel word embeddings based on a simple and intuitive rolling average. Still in dev mode.
Tic tac toe dataset and PyTorch baseline.
Useless project that tries to model the life of different bacterias in a petri dish: for learning purpose
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Build interactive, publication-quality documents from Jupyter Notebooks
High-Level Training, Data Augmentation, and Utilities for Pytorch
Visualise european soccer player's data
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
Datasets, Transforms and Models specific to Computer Vision
Code for generating datasets consisting of overlayed images.
Geometric Deep Learning Extension Library for PyTorch