mtanghu Goto Github PK

active-passive-losses

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

attention-advice

Transformers with learned advice vectors

awd-lstm-lm

LSTM and QRNN Language Model Toolkit for PyTorch

blockchain_video

COM 217 video presentation code for an explainer on how blockchain works using Manim

citadel-central-datathon-fall21

2nd place winning analysis of smoking data for the Citatdel Central Datathon of Fall 2021 (final report included)

datasets

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

dni-pytorch

Decoupled Neural Interfaces using Synthetic Gradients for PyTorch

dni-rnn

Decoupled Neural Interfaces (Jaderberg et al. 2017) mini-package for easy integration with pytorch RNNs

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

fastformer

A pytorch &keras implementation and demo of Fastformer.

flash-attention

Fast and memory-efficient exact attention

flops-profiler

pytorch-profiler

hccpy

A Python implementation of Hierarchical Condition Categories

leap

LEAP: Linear Explainable Attention in Parallel for causal language modeling with O(1) path length, and O(1) inference

martingale

quick simulation to see how martingale betting would work with realistic conditions (i.e. finite but large money), as well as removing finite stopping condition

mega-pytorch

Implementation of Mega, the Single-head Attention with Multi-headed EMA architecture that currently holds SOTA on Long Range Arena

parallelizing_linear_rnns

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rethinking-neural-computation

Draft & experiments for an alternative approach to neuro-symbolic AI that allows for "thinking fast and slow"

rwkv-cuda

The CUDA version of the RWKV language model ( https://github.com/BlinkDL/RWKV-LM )

rwkv-lm

RWKV is a RNN with transformer-level performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

sgconv

smart-on-fhir-tutorial

SMART on FHIR developer tutorial

sru

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

tinygrad

You like pytorch? You like micrograd? You love tinygrad! ❤️

transformer-trader

Investigation into whether Transformers and self-supervised learning could be used to trade currency markets

urf

URF: Unsupervised Random Forest fork that uses scikit learn instead of pycluster for ~100x speed up