PyNeuraLogic
typedb-ml
PyNeuraLogic | typedb-ml | |
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7 | 1 | |
267 | 548 | |
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8.0 | 0.0 | |
6 days ago | 6 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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PyNeuraLogic
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[P] PyNeuraLogic - a framework for writing differentiable logic programs
Hi, sure. With this framework, you can write and train deep learning models similarly to PyTorch or TensorFlow. Although the main aim of PyNeuraLogic is on deep relational learning and it uses custom declarative language (implemented in Python). Best fitting use cases are everything where you can utilize relations. One of those use-cases that we are promoting right now is on Graph Neural Networks (GNNs), where you have relations between nodes (such as social networks, molecules). You can then utilize those relations and do regular tasks on graphs, such as link prediction, graph classification, node classification, etc. GNNs quite nicely fit the framework and its language and can be expressed just in one line (as shown in the README). The concrete use-case of PyNeuraLogic on GNNs could then be a molecule classification (example). Other use-cases could be for NLP (we have todo to write an example for it) or knowledge base completion. You could also use it like a regular framework without utilizing relations, but in that case, it might be more efficient to go with PyTorch or TensorFlow.
- Show HN: Evaluate Deep Learning models directly in a database with PyNeuraLogic
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Why Hypergraphs? (2013)
For an original proposal that do logic inference on Hypergraphs I am using NeuraLogic, through a Python frontend (https://github.com/LukasZahradnik/PyNeuraLogic)
I wonder if this is something the author would have enjoyed…
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This Week in Python
PyNeuraLogic – PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- GitHub - LukasZahradnik/PyNeuraLogic: PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- Show HN: PyNeuraLogic: Python Differentiable Logic Programs
typedb-ml
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Graph theory, graph convolutional networks, knowledge graphs
It's always funny to see people mentioning hypergraphs in relation to knowledge graphs, this is exactly what we do at Grakn Labs (disclaimer: work there) https://grakn.ai
For others: we're also starting to look into ML on knowledge graphs, check out our initial work at https://github.com/graknlabs/kglib :D
What are some alternatives?
reloadium - Hot Reloading and Profiling for Python
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
chemicalx - A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
dgl-ke - High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
hatch - Modern, extensible Python project management
0xDeCA10B - Sharing Updatable Models (SUM) on Blockchain
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
TrainInvaders - 👾 Jupyter Notebook + Space Invaders!?
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
GPT2-api - 🤖 (Easily) run your own GPT-2 API. Post writing prompts, get AI-generated responses
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
ANN-decompiler - "AI" demystified: a decompiler