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This blog post explores a technique to make legacy Fortran simulation code differentiable using LFortran, Enzyme, and Tesseract, allowing automatic differentiation and integration with JAX for use in machine learning pipelines.
Introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for compressing deep neural network layers via small core tensors, achieving high compression ratios while maintaining accuracy.
This article explores neural cellular automata as a computational model inspired by biological morphogenesis and regeneration, demonstrating how simple local rules can lead to complex global behaviors.
This paper presents 'Additive Atomic Forests,' a framework for simultaneous symbolic recovery of functions and their antiderivatives using derivative algebra and self-expanding atom libraries. The method achieves strong performance on classification benchmarks and Feynman symbolic regression tasks while offering interpretable results.
This paper presents Block-Wise Differentiable Sinkhorn Attention, a method for efficient long-context balanced entropic optimal transport attention on TPU hardware. It introduces a tail-refinement surrogate for exact differentiation, proving an efficient backward pass schedule and demonstrating significant improvements in Pfam sequence alignment reconstruction.