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Machine Learning Engineering Open Book
Reinforcement learning environments for classical, intuitionistic, and modal first-order connection calculi.
Benchmarking framework for protein representation learning. Includes a large number of pre-training and downstream task datasets, models and training/task utilities. (ICLR 2024)
Source code associated with "Spatio-relational inductive biases for spatial cell type deconvolution"
pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active le…
GearNet and Geometric Pretraining Methods for Protein Structure Representation Learning, ICLR'2023 (https://arxiv.org/abs/2203.06125)
A playbook for systematically maximizing the performance of deep learning models.
Repository for Distributed representations of graphs for drug pair scoring
Code base acompanying the paper Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
TigerLily: Finding drug interactions in silico with the Graph.
Paper list for equivariant neural network
GP Sinkhorn Implementation, paper: https://www.mdpi.com/1099-4300/23/9/1134
High-performance Vision library in Python. Scale your research, not boilerplate.
Mediterranean Machine Learning school tutorials
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Example code and script for using Cam-HPC
PyTorch code to run synthetic experiments.
NetworKit is a growing open-source toolkit for large-scale network analysis.
An open source multi-tool for exploring and publishing data
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Training and evaluating a variational autoencoder for pan-cancer gene expression data
Literature of deep learning for graphs in Chemistry and Biology