snorkel
dgl
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| snorkel | dgl | |
|---|---|---|
| 5 | 4 | |
| 5,707 | 12,999 | |
| 0.8% | 1.5% | |
| 5.5 | 9.9 | |
| about 2 months ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
snorkel
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
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Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
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[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
dgl
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
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Detecting Out-of-Distribution Datapoints via Embeddings or Predictions
For trees/graphs, you’ll want a neural net that can take these as inputs for which I’m not sure a standard library exists. One recommendation is to checkout dgl: https://github.com/dmlc/dgl
- Beyond Message Passing: A Physics-Inspired Paradigm for Graph Neural Networks
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[D] Convenient libs to use for new research project at the intersection of GNN and RL.
The best pkg for GCN - https://github.com/dmlc/dgl
What are some alternatives?
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
pytorch_geometric - Graph Neural Network Library for PyTorch
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
torchdrug - A powerful and flexible machine learning platform for drug discovery
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
spektral - Graph Neural Networks with Keras and Tensorflow 2.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)