Stars
[UAI'24 Oral] Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
An exploration of the state of the art in the application of data science to quantum chemistry.
Python library to determine the point group of molecular geometries
SAM & SAM 2 for Medical Image Segmentation: Open-Source Project Summary
New version of dft-book for Quantum Espresso
Official public repository for the XtalOpt crystallographic multi-objective evolutionary algorithm
List of Molecular and Material design using Generative AI and Deep Learning
Integer Programming encoding for Crystal Structure Prediction with classic and quantum computing bindings
Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular interactions with AlphaFold3" in PyTorch
A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.
Gradient-free optimization method for the multidimensional arrays and discretized multivariate functions based on the tensor train (TT) format.
This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search (ICML 2018)
Курс "Глубокое обучение (Deep Learning)" (ВМК, МГУ имени М.В. Ломоносова)
We implement tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order operands.
A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
📝 Some source code about matrix multiplication implementation on CUDA
Library for training Gaussian Processes on Molecules
Heteroscedastic Bayesian Optimisation in Numpy
A neural network training framework within a task-based parallel programming paradigm
Modern C++ Programming Course (C++03/11/14/17/20/23/26)
Self-Tuning Optimized Kalman Filtering (STOK) + DyNet simulation + connectivity metrics
Adaptive Control System Using ML-Tunable Kalman Filters
A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.