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Fully open reproduction of DeepSeek-R1
Standardized data set for machine learning of protein structure
This repo contains the codes for our paper Conditional Antibody Design as 3D Equivariant Graph Translation.
Translating Akkadian signs to transcriptions using NLP techniques such as HMM, MEMM and BiLSTM neural networks.
Educational implementation of the Discrete Flow Matching paper
Shrinks ONNX files by quantizing large float constants into eight bit equivalents.
An action which manages a github release
This is the supporting data and code for the pre-print "Recurrent neural chemical reaction networks that approximate arbitrary dynamics": https://doi.org/10.48550/arXiv.2406.03456
The libgphoto2 camera access and control library.
Latex code for making neural networks diagrams
Source code and data for The Economist's Ukraine war-fire model
The first AI agent that builds permissionless integrations through reverse engineering platforms' internal APIs.
An overview of algorithms for estimating pseudotime in single-cell RNA-seq data
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
Code for the paper https://arxiv.org/abs/2402.04997
List of papers about Proteins Design using Deep Learning
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.
Chai-1, SOTA model for biomolecular structure prediction
How to build your own Kindle 4 photo frame displaying your Home Assistant Lovelace panel
Generation of protein sequences and evolutionary alignments via discrete diffusion models
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Simple, efficient, open-source package for Simultaneous Localization and Mapping
An open-source RAG-based tool for chatting with your documents.
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: …
Listing of papers about machine learning for proteins.
A package for computing data-driven approximations to the Koopman operator.