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A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
S2ORC: The Semantic Scholar Open Research Corpus: https://www.aclweb.org/anthology/2020.acl-main.447/
LAMMPS tutorials for Beginners
Python library for reading and writing image data
An evaluation framework for machine learning models simulating high-throughput materials discovery.
This repository includes the official implementation of OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs.
Software design principles for machine learning applications
A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Models
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型
An open-source, low-code machine learning library in Python
SoftVC VITS Singing Voice Conversion
Natural Gradient Boosting for Probabilistic Prediction
A python library for user-friendly forecasting and anomaly detection on time series.
XAI - An eXplainability toolbox for machine learning
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Optax is a gradient processing and optimization library for JAX.
Bayesian Modeling and Probabilistic Programming in Python
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, …
Deep universal probabilistic programming with Python and PyTorch
[NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
A highly efficient implementation of Gaussian Processes in PyTorch
Examples for https://github.com/optuna/optuna