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Pacific Northwest National Laboratory
- https://orcid.org/0000-0002-4199-7244
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Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
A collection of important graph embedding, classification and representation learning papers with implementations.
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Toolbox of models, callbacks, and datasets for AI/ML researchers.
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
[IJCAI'18] Spatio-Temporal Graph Convolutional Networks
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
SchNetPack - Deep Neural Networks for Atomistic Systems
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
NequIP is a code for building E(3)-equivariant interatomic potentials
Calculate Root-mean-square deviation (RMSD) of two molecules, using rotation, in xyz or pdb format
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
Python version of the amazing Reaction Mechanism Generator (RMG).
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data
JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ
Keras implementation of a ResNet-CAM model
Fastest Gephi's ForceAtlas2 graph layout algorithm implemented for Python and NetworkX
Representation learning on dynamic graphs using self-attention networks
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
[ECCV2024] This is an official implementation for "PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model"
A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.