3DSS is the first differentiable surface splatting renderer that recovers shape, spatially-varying BRDF materials, and HDR illumination from multi-view images via a coverage-based compositing model derived from reconstruction kernels.
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FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.
citing papers explorer
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3DSS: 3D Surface Splatting for Inverse Rendering
3DSS is the first differentiable surface splatting renderer that recovers shape, spatially-varying BRDF materials, and HDR illumination from multi-view images via a coverage-based compositing model derived from reconstruction kernels.
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A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations
FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
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A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.
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Mapping the Winds of Stance Dynamics using Potential Landscape Models
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.