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- # discrete-differentiable-networks
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+ # ∂B nets
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[ ![ Python package] ( https://github.com/Z80coder/discrete-differentiable-networks/actions/workflows/python.yaml/badge.svg )] ( https://github.com/Z80coder/discrete-differentiable-networks/actions/workflows/python.yaml )
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- A Boolean Logic Network library for learning boolean functions on GPUs with gradient descent.
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+ A nerual network library for learning boolean-valued, discrete functions on GPUs with gradient descent.
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- The working prototype is implemented in Wolfram. The production library is implemented in Python .
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+ The library is implemented in Python using the [ Flax ] ( https://github.com/google/flax ) and [ JAX ] ( https://github.com/google/jax ) frameworks .
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Questions? Ask @Z80coder
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+ ## Draft paper
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+ [ "∂B nets: learning discrete functions by gradient descent"] ( ./docs/db.pdf ) (March 2023).
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## Demos
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- Neural logic nets
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+ [ ∂B nets] ( https://drive.google.com/file/d/1UUhv6loBrFnZ7jwiHBofnp06at_8bm_F/view?usp=share_link ) (1 hour).
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+ # Prototype
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+ The working prototype was implemented in Wolfram. The demos below were snapshots of work-in-progress.
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+ ## Demos
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- [ Neural logic nets] ( https://drive.google.com/file/d/1_IECuI0f58o_aIIdaQhRo6qPH517YaMa/view?usp=share_link ) (15m)
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- ## Development videos
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+ ## Development snapshots
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- [ The Soft-NOT operator] ( https://drive.google.com/file/d/1z2WFpz4eWLb9xauRnIl6mSXhkbU-XR6X/view?usp=share_link ) (10m)
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- [ The Soft-AND operator] ( https://drive.google.com/file/d/1l9Y2cWJYYdYSsgqwfH-Dfo2Nxmiewia-/view?usp=share_link ) (10m)
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- [ Boolean logic nets and MNIST] ( https://drive.google.com/file/d/12Rwx8H76UTNRdBK4WAwe_QeTWiGrbP-_/view?usp=share_link ) (18m)
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- [ Neural logic nets for differentiable QL] ( https://drive.google.com/file/d/15rAagCh7LxEN0CHVNkTY6iPWSxrAG0pW/view?usp=share_link ) (30m)
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- More to come!
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