Popular GitHub Repositories for Condensed Matter

SchNetPack - Deep Neural Networks for Atomistic Systems

Pytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"

AAAI 2025: Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model

A Compositional Object-Based Approach to Learning Physical Dynamics

Test and evaluate LLMs and model configurations, across all the scenarios that matter for your application

Code for "Deconstructing Monocular Depth Reconstruction: The Design Decisions that Matter" (https://arxiv.org/abs/2208.01489)

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.

Text Encoders finally matter ?? - scale CLIP & LLM influence! + a Nerdy Transformer Shuffle node

Easy multiple sclerosis white matter lesion segmentation using convolutional deep neural networks.

Spinal cord gray matter segmentation using deep dilated convolutions.

The details that matter: Frequency resolution of spectrograms in acoustic scene classification - paper replication data

Multi agent reinforcement learning for intelligent active matter

Go SDK for Anthropic's Claude, a next-generation AI assistant for your tasks, no matter the scale.

Transformer-guided diffusion for galaxy clustering. Code repository associated with https://arxiv.org/abs/2311.17141

Open-source test generation SDK for LLM applications. Access curated test sets. Build context-specific test sets and collaborate with subject matter experts.

A powerful tool that converts voice recordings into high-quality Anki flashcards using AI-powered transcription and LLM processing, featuring Few-Shot Learning to adapt to your personal style and supporting any language or subject matter.

The Project AI Summarizer App is a powerful tool that harnesses the capabilities of AI to provide efficient and accurate text summarization. It utilizes advanced natural language processing models to analyze the input text and generate a condensed summary that captures the essential information.

AICONSlab's white matter hyperintensities (WMH) segmentation algorithm using CNNs

Source Code for TrustCom2022 Accepted Paper " 'Comments Matter and The More The Better': Improving Rumor Detecion with User Comments".

This is provisional codes for a conference paper titled "Robustness of Probabilistic U-Net for Automated Segmentation of White Matter Hyperintensity in Different Datasets of Brain MRI".