CrabNet VS mat2vec

ProteinStructurePrediction - Protein structure prediction is the task of predicting the 3-dimensional structure (shape) of a protein given its amino acid sequence and any available supporting information. In this section, we will Install and inspect sidechainnet, a dataset with tools for predicting and inspecting protein structures, complete two simplified implementations of Attention based Networks for predicting protein angles from amino acid sequences, and visualize our predictions along the way.

GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)

query-selector - LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

ViTGAN - A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.

Invariant-Attention - An implementation of Invariant Point Attention from Alphafold 2

Perceiver - Implementation of Perceiver, General Perception with Iterative Attention

hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.

SAITS - The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

data-resources-for-materials-science - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

attention_to_gif - Visualize transition of attention weights across layers in a Transformer as a GIF