pytorch_geometric_temporal
awesome-drug-pair-scoring
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pytorch_geometric_temporal | awesome-drug-pair-scoring | |
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18 | 4 | |
2,484 | 85 | |
- | - | |
1.8 | 0.0 | |
12 days ago | over 1 year ago | |
Python | ||
MIT License | Apache License 2.0 |
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pytorch_geometric_temporal
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Ask HN: ML Papers to Implement
I have done this a few times now. Alone (e.g. https://github.com/paulmorio/geo2dr) and in collaboration with others (e.g. https://github.com/benedekrozemberczki/pytorch_geometric_tem...) primarily as a way to learn about the methods I was interested in from a research perspective whilst improving my skills in software engineering. I am still learning.
Starting out I would recommend implementing fundamental building blocks within whatever 'subculture' of ML you are interested in whether that be DL, kernel methods, probabilistic models, etc.
Let's say you are interested in deep learning methods (as that's something I could at least speak more confidently about). In that case build yourself an MLP layer, then an RNN layer, then a GNN layer, then a CNN layer, and an attention layer along with some full models with those layers on some case studies exhibiting different data modalities (images, graphs, signals). This should give you a feel for the assumptions driving the inductive biases in each layer and what motivates their existence (vs. an MLP). It also gives you the all the building blocks you can then extend to build every other DL layer+model out there. Another reason is that these fundamental building blocks have been implemented many times so you have a reference to look to when you get stuck.
On that note: here are some fun GNN papers to implement in order of increasing difficulty (try building using vanilla PyTorch/Jax instead of PyG).
- GitHub - benedekrozemberczki/pytorch_geometric_temporal: PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
- PyTorch Geometric Temporal 0.37
- PyTorch Geometric Temporal - Spatiotemporal Signal Processing with Neural Machine Learning Models
- [P] PyTorch Geometric Temporal
- Show HN: Deep Learning for Windmill Output Forecasting with PyTorch
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[R] PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Repo: https://github.com/benedekrozemberczki/pytorch_geometric_temporal
- PyTorch Geometric Temporal 0.27
- Show HN: Machine Learning on Spatiotemporal Data – PyTorch Geometric Temporal
- PyTorch Geometric Temporal
awesome-drug-pair-scoring
- A Unified View of Relational Deep Learning for Drug Pair Scoring
- Show HN: A Unified Model of Deep Learning for Drug Pair Scoring
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[R]
Git: https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey
- Show HN: Drug-drug interaction, synergy, and polypharmacy prediction
What are some alternatives?
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
efficient-gnns - Code and resources on scalable and efficient Graph Neural Networks
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diffnet - Graph Neural Network based Social Recommendation Model. SIGIR2019.
torchdrug - A powerful and flexible machine learning platform for drug discovery
PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
graphein - Protein Graph Library
typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB
gnn - TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
GNNPapers - Must-read papers on graph neural networks (GNN)
pytorch_geometric - Graph Neural Network Library for PyTorch
radium-formula-parser