Popular GitHub Repositories for Structural Alignments

A collection of important graph embedding, classification and representation learning papers with implementations.

[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.

LAYRA is a ready-to-use visual RAG system with a complete UI built with Next.js and FastAPI, preserving document layout, tables, paragraphs, and graphical elements without any structural fragmentation.

Automated modeling and machine learning framework FEDOT

Generation of protein sequences and evolutionary alignments via discrete diffusion models

No-Frills Functional Programming Lib Augmenting Javascript/Node.js

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

LucaProt: A novel deep learning framework that incorporates protein amino acid sequence and structural information to predict protein function.

A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".

Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs

Neural Networks for Protein Sequence Alignment

MEt3R: Measuring Multi-View Consistency in Generated Images

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

RiboNucleic Acid (RNA) Language Model

[ICRA 2021] Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning