miniF2F
tensor_annotations
miniF2F  tensor_annotations  

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miniF2F

[D] Have their been any attempts to create a programming language specifically for machine learning?
That said, you *can* write down a desired type and have a system write down a ton of type annotations or generate a bunch of code to prove that the type you wrote down is satisfied by your program. There's been recent work on this in deep learning for theorem proving, such as this work which uses GPT for proving theorems in Lean, a dependently type programming language and theorem prover. A better approach though would be to combine this with an actual tree search algorithm to allow a more structured search over the space of proofs, instead of trying to generate full correct proofs in one shot. Hypertree Proof Search does this, using a variant of AlphaZero to search and finetune the neural net. Unfortunately it hasn't been opensourced though, and it's pretty compute intensive, so we can't use this for actual type inference yet. But yeah there's active interest in doing this kind of thing, both as a proving ground for using RL for reasoning tasks and from mathematicians for theoremproving.
 [D] First Author Interview: AI & formal math (Formal Mathematics Statement Curriculum Learning)
 [D] OpenAI tackles Math  Formal Mathematics Statement Curriculum Learning (Paper Explained Video)
 MiniF2F
tensor_annotations

[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrickkidger/torchtyping or https://github.com/deepmind/tensor_annotations

Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
What are some alternatives?
einops  Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dexlang  Research language for array processing in the Haskell/ML family
torchtyping  Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
FL  FL language specification and reference implementations
TablaM  The practical relational programing language for dataoriented applications
tinycudann  Lightning fast C++/CUDA neural network framework
jaxtyping  Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/
MindsDB  The platform for customizing AI from enterprise data
hasktorch  Tensors and neural networks in Haskell