query-selector
CrabNet
| query-selector | CrabNet | |
|---|---|---|
| 1 | 1 | |
| 75 | 81 | |
| - | - | |
| 3.7 | 3.7 | |
| 6 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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query-selector
CrabNet
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Artificial intelligence can revolutionise science
I don't know. As for "literature-based discovery," this project/paper sounded like a pretty big deal when it came out a few years ago: https://github.com/materialsintelligence/mat2vec . And I see this thing came out more recently: https://github.com/anthony-wang/CrabNet .
Of course not all fields lend themselves as well to this as does materials science.
What are some alternatives?
neural_prophet - NeuralProphet: A simple forecasting package
Invariant-Attention - An implementation of Invariant Point Attention from Alphafold 2
LaTeX-OCR - pix2tex: Using a ViT to convert images of equations into LaTeX code.
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
sktime - A unified framework for machine learning with time series
mat2vec - Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).
how-do-vits-work - (ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
gluonts - Probabilistic time series modeling in Python
ViTGAN - A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.