engyasin
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Deep networks
This is the pytorch implementation of some representative action recognition approaches including I3D, S3D, TSN and TAM.
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"
This repository contains demos I made with the Transformers library by HuggingFace.
A curated collection of adversarial attack and defense on graph data.
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
Reading list for research topics in multimodal machine learning
A collection of research papers and software related to explainability in graph machine learning.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation
Hackable and optimized Transformers building blocks, supporting a composable construction.
Code for "Transformer Networks for Trajectory Forecasting"
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
A toolkit for reproducible reinforcement learning research.
Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Implementation of Multi-Game Decision Transformers in PyTorch
A curated list of Decision Transformer resources (continually updated)
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Solving synthetic 2d path-planning problems with a convolutional neural network.
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
Paper reading notes on Deep Learning and Machine Learning
A concise but complete full-attention transformer with a set of promising experimental features from various papers