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2,896 papers
Across CIFAR-100-C, Tiny-ImageNet-C, and SEN12MS, this work shows reliable target-domain coverage transfer without labeled target calibration data, with label-free diagnostics that predict when correction is needed.
Food structure and its effect on deformation behaviour and sensory perception is still a challenge in food science. Small-Amplitude Oscillatory Shear (SAOS) is a common method for the determination of the linear viscoelastic properties and stability of food materials, but it is not applicable to large deformations, suc...
It is argued that a third, largely untapped signal is both richer and more principled: critical adjacency, the pairwise relation established when an expert critic explicitly links two artists in long-form prose, which offers both a scalable cold-start discovery mechanism and a sociological grounded approach to MIR and...
We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for navigating Reidemeister moves. The pipeline applies to arbitrary knots and links; we test it on ``very hard'' unknot diagrams and, using diagram inflation, on $4_1\#9_{10}$ where w...
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Many high-dimensional datasets concentrate near a low-dimensional structure embedded in the ambient space. Generative models for such data must control off-support mass while remaining computationally practical. Diffusion models use iterative denoising at inference, whereas standard normalizing flows require invertible...
A universal approximation theorem is established for DFNNs, advancing the state-of-the-art of neural network approximation theory to general metric-space-valued responses, without making model assumptions or relying on local smoothing.
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. We study co...
An overview of the theory of overparameterized ML is provided by focusing on explaining the most foundational findings through a statistical signal processing perspective and emphasizing the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open frontiers that...
This work proposes a regularized maximum likelihood framework for simultaneous parameter estimation and feature selection in MoE whose experts belong to the generalized linear model family, covering Gaussian, Poisson and multinomial responses within a single formulation.
Heterogeneous cardiac signals can mutually supervise a single foundation model of cardiac physiology using a physiology-aware joint-embedding predictive architecture built on a physiology-aware joint-embedding predictive architecture.
The central result recovers the KL order as the decay rate of the directional Fisher quadratic form approaching the singularity, in original coordinates, without a Hironaka resolution.
This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly nonmonotone stochastic composite inclusions. Unlike unbiased estimators such as mini-batching, developing stochastic biased variants faces a fundamental technical challenge and h...
General Science
General Science
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
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