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Simple Distributed Reinforcement Learning Framework（シンプルな分散強化学習フレームワーク）
Adaptive Second Order Optimizer using Fisher Information
Modularized Implementation of Deep RL Algorithms in PyTorch
Explorer is a PyTorch reinforcement learning framework for exploring new ideas.
A faster more robust algorithm for learning temporal predictions
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
Awesome Incremental Learning
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
Demonstrations of Loss of Plasticity and Implementation of Continual Backpropagation
Official repository of Evolutionary Optimization of Model Merging Recipes
BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection
Summaries for exciting works in the field of Deep Learning.
Continual Learning papers list, curated by ContinualAI
Repository of code for the experiments for the ICLR submission "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Networks"
Evaluate three types of task shifting with popular continual learning algorithms.
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
GPT4All: Chat with Local LLMs on Any Device
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Source code for Twitter's Recommendation Algorithm
The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering