InferOpt.jl
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Combinatorial optimization layers for machine learning pipelines
Bumps [julia-actions/setup-julia](https://github.com/julia-actions/setup-julia) from 1 to 2. Release notes Sourced from julia-actions/setup-julia's releases. v2.0.0 - Update to Node20 What's Changed update to node20 by @ranocha in julia-actions/setup-julia#209 Note the rationale for...
- [x] Use `DocStringExtensions.jl` - [x] Separate API between specific problems, losses, optim layers and internals - [x] Background page for math (optim + ML), focus on discrete functions rather...
High priority: - Use https://github.com/JuliaDocs/DocStringExtensions.jl - Background page for math (optim + ML), focus on discrete functions rather than linear programs - Guide for choosing the right tool (as in...
Testing the waters to see if #40 has been solved by the latest release of FrankWolfe mentioned in https://github.com/ZIB-IOL/FrankWolfe.jl/issues/387
Frank-Wolfe related tests fail due to the linear solver for implicit differentiation not converging Main suspect: https://github.com/ZIB-IOL/FrankWolfe.jl/issues/387
I must make it easier for contributors to understand how chain rules are written. This requires: - basics of autodiff and VJPs - formulas
At the moment it's not clear in the documentation which kwargs are given to which pipeline element at which time. Even I'm a bit lost ^^
We urgently need a better documentation with - a better intro to the theory - examples for all types of loss functions - examples for using kwargs to pass instances...
Since I figured out how to use `JET.report_package` properly, it throws a few errors for interface functions that are not implemented. For now the test is skipped, but we need...
We should add some tests checking that gradients computed by our rrules are correct. For this, we can use [FiniteDifferences.jl](https://github.com/JuliaDiff/FiniteDifferences.jl) or [ChainRulesTestUtils.jl](https://github.com/JuliaDiff/ChainRulesTestUtils.jl).