Popular GitHub Repositories for Weight Decay

MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

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.

Jcseg is a light weight NLP framework developed with Java. Provide CJK and English segmentation based on MMSEG algorithm, With also keywords extraction, key sentence extraction, summary extraction implemented based on TEXTRANK algorithm. Jcseg had a build-in http server and search modules for lucene,solr,elasticsearch,opensearch

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

An AI memory layer with short- and long-term storage, semantic clustering, and optional memory decay for context-aware applications.

? [BMVC 2022] You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction. SOTA for low light enhancement, 0.004 seconds try this for pre-processing.

SlickGPT is a light-weight "use-your-own-API-key" web client for the OpenAI API written in Svelte. It offers GPT-4 integration, a userless share feature and other superpowers.

A light-weight, power efficient, and general purpose convolutional neural network

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

A flexible, adaptive classification system for dynamic text classification

[NeurIPS 2024]OmniTokenizer: one model and one weight for image-video joint tokenization.

PyTorch implementation of Data Free Quantization Through Weight Equalization and Bias Correction.

? CHONK your texts with Chonkie ✨ Type-friendly, light-weight, fast and super-simple chunking library

pyTorch implementation of Spectral Normalization for Generative Adversarial Networks

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

Continual Hyperparameter Selection Framework. Compares 11 state-of-the-art Lifelong Learning methods and 4 baselines. Official Codebase of "A continual learning survey: Defying forgetting in classification tasks." in IEEE TPAMI.

Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Official repository for the paper "A Modern Self-Referential Weight Matrix That Learns to Modify Itself" (ICML 2022 & NeurIPS 2021 Deep RL Workshop) and "Accelerating Neural Self-Improvement via Bootstrapping" (ICLR 2023 Workshop)