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3 results for “differentiable”
LaPrune: Controllable Differentiable Sparsity at Million Scale
LaPrune is a new differentiable sparsity method introduced in an arXiv preprint that enables exact budget control over model component selection while preserving gradient flow and offering theoretical guarantees on mask hardness and mass preservation.
Aug 6, 2026
Geometry-Aware R-Structured Kolmogorov-Arnold Networks
Researchers introduced GRS-KAN, a new neural architecture that embeds differentiable R-functions into Kolmogorov-Arnold Networks to explicitly encode geometric constraints and discontinuities, improving accuracy and interpretability on benchmark regression tasks with structured boundaries.
Published Jul 3, 2026 · Analyzed Jul 6, 2026
Neural Render Proxies for Interactive and Differentiable Lighting
A forum thread on Hacker News titled 'Neural Render Proxies for Interactive and Differentiable Lighting' contains user comments discussing a technical concept in neural rendering, with no original article, reporting, or verifiable claims provided.
Published Jul 1, 2026 · Analyzed Jul 6, 2026