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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.