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amdim-public

Public repo for Augmented Multiscale Deep InfoMax representation learning

cmu-multimodalsdk

CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.

conditional_infonce

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fair-mixup

ICLR 2021, Fair Mixup: Fairness via Interpolation

graphcl

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

lcifr

Learning Certified Individually Fair Representations

mae_tpu

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

mae_understand

Code repo for the CVPR paper, "Understanding Masked Autoencoders via Hierarchical Latent Variable Models""

matrix_sinfo_scripts

Credit: homles; script to get whatever nodes you want on matrix. The script allows you to request nodes for specific cpu, ram, vram, gpu-archi, and exclude nodes that you don’t want

meta-project

Code for meta project forked from Siyuan codebase.

mini-tool-box

Some util functions for machine learning experiments

minnn-assignment

An assignment on creating a minimalist neural network toolkit for CS11-747

moco-v3

PyTorch implementation of MoCo v3 https//arxiv.org/abs/2104.02057

multimodal_routing

python-latex-highlighting

LaTeX style for Python highlighting

relative_predictive_coding

Project page for paper Self-supervised Representation Learning with Relative Predictive Coding

self_explaining_structures_improve_nlp_models

simclr-2

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.

simsiam

PyTorch implementation of SimSiam https//arxiv.org/abs/2011.10566

tutorials

Some tutorials I've created for myself and my collaborators

vdpc

Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"