Derives geodesic ridge regularization and Riemannian Gibbs Process prior for feature-learning wide neural networks, generalizing kernel-regime results via function-space axiomatization.
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Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32
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Subliminal learning occurs via compatible auxiliary and class output heads on task-unrelated inputs, even with random hidden layers or architecture changes, with theory and upper bounds on failure.
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
SPA unlocks patch-level features in CLIP for class-incremental learning via semantic-guided selection and optimal transport alignment with class descriptions, plus projectors and pseudo-feature replay to reduce forgetting.
Spectral Energy Centroid is a new metric that quantifies signal frequency and INR spectral bias, supporting better hyperparameter selection and cross-architecture analysis.
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
Sublinear neural networks parametrize convex sets by learning their support and gauge functions, backed by a universal approximation theorem and tested on shape optimization tasks.
MARS parallel reservoirs achieve up to 21x training speedups and outperform LRU, S5, and Mamba on long sequence benchmarks while remaining gradient-free and compact.
Physics-augmented neural networks act as stable, thermodynamically consistent surrogates for microscale problems, enabling simultaneous optimization of macroscale material layout and microscale descriptors in nonlinear finite-strain anisotropic hyperelastic structures.
MemDLM embeds a simulated denoising trajectory into DLM training via bi-level optimization, creating a parametric memory that improves convergence and long-context performance even when the memory is dropped at test time.
FBApro computes the nearest steady-state flux distribution to a reference vector via a closed-form linear projection derived from orthogonal projections onto affine spaces.
AVIS applies autoregressive diffusion models to video inverse problems by streaming restoration with measurement-consistent initialization, reducing latency from 114s to 4s and raising throughput to 1.18 FPS (or 5.91 FPS in the Flash variant).
GeoHand adapts priors from a general-scene geometry estimator via a GeoAdapter, gated fusion, and keypoint-queried refiner to reach SOTA monocular 3D hand reconstruction on FreiHAND, DexYCB, and HO3Dv3 under heavy occlusion.
Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.
DMI-Lib delivers 0.4-6.8% overhead for offline batch LLM inference and ~6% for moderate online serving while exposing rich internal signals across backends, cutting latency overhead 2-15x versus prior observability baselines.
DuetFair couples inter-subgroup adaptation with intra-subgroup robustness via FairDRO (dMoE plus subgroup-conditioned DRO) to boost worst-case and equity-scaled performance on medical segmentation benchmarks.
An optimal control formulation adds time-dependent perturbations to the reverse diffusion process to match target attribute distributions while preserving sample fidelity.
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
ARS shapes reasoning trace representations by clustering states that produce consistent answers and separating those that produce inconsistent ones via latent perturbations, improving plug-and-play hallucination detection without human annotations.
The paper presents stable-worldmodel (swm), a platform with high-performance data layer, modern world model baselines, planning solvers, and extended environments for reproducible research and generalization evaluation.
MahaVar augments the Mahalanobis OOD score with class-wise distance variance, which is theoretically higher for in-distribution samples under relaxed Neural Collapse geometry.
SymADiT generates stable symmetric materials by enforcing Wyckoff-position and space-group constraints inside a latent generative model built on the prior ADiT architecture.
U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.
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Parametrizing Convex Sets Using Sublinear Neural Networks
Sublinear neural networks parametrize convex sets by learning their support and gauge functions, backed by a universal approximation theorem and tested on shape optimization tasks.