Stars
TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients.
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
A modular graph-based Retrieval-Augmented Generation (RAG) system
Convert any text to a graph of knowledge. This can be used for Graph Augmented Generation or Knowledge Graph based QnA
We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024) and GPT-4o.
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
Official implementation of “Code Recommendation for Open Source Software Developers" at The Web Conference 2023 (WWW 2023).
SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]
ICCAD'23 Best Paper Award candidate: Robust GNN-based Representation Learning for HLS
UCLA-DM / HLSyn
Forked from ZongyueQin/HLSynHLSyn benchmark for paper "Towards a Comprehensive Benchmark for FPGA Targeted High-Level Synthesis"
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
Verilog evaluation benchmark for large language model
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
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.
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction. ICML 2020 Graph Representation Learning and Beyond (GRL+) Workshop
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Implementation of "Deep Graph Matching Consensus" in PyTorch
This is the repository for PIPR. This repository contains the source code and links to some datasets used in the ISMB/ECCB-2019 paper "Multifaceted Protein-Protein Interaction Prediction Based on S…
A Learning based Branch and Bound for Maximum Common Subgraph related Problems
This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)