OpenROAD's unified application implementing an RTL-to-GDS Flow. Documentation at https://openroad.readthedocs.io/en/latest/

A Python library for working with logic networks, synthesis, and optimization.

Hierarchical graph clustering

Papers about large graph models.

Must-read Papers on LLM Agents.

AlphaZero for continuous control tasks

Macro Placement - benchmarks, evaluators, and reproducible results from leading methods in open source

A framework for hydrodynamics explorations and prototyping

RePlAce global placement tool

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

PyTorch implementations of deep reinforcement learning algorithms and environments

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Implementations of DeepPlace, PRNet, HubRouter, PreRoutGNN, FlexPlanner and DSBRouter.

PathwayGNN: work on the data extracting from Reactome

Tree edit distance using the Zhang Shasha algorithm

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

A collection of some materials of knowledge graph question answering

Insightful Tutorials and Papers about Knowledge Graphs

PyTorch implementation of DEC (Deep Embedding Clustering)

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

A collection of research papers and software related to explainability in graph machine learning.

Pytorch-Named-Entity-Recognition-with-BERT

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.