arXiv:2506.10177v3 Announce Type: replace Abstract: Diffusion-based generative models employ stochastic differential equations (SDEs) and their equivalent probability flow ordinary differential equations (ODEs) to establish a smooth transformation between complex high-dimensional data distributions and tractable prior distributions. In this paper, we…
arXiv:2512.10191v1 Announce Type: new Abstract: Non-random missing data is a ubiquitous yet undertreated flaw in multidimensional time series, fundamentally threatening the reliability of data-driven analysis and decision-making. Pure low-rank tensor completion, as a classical data recovery method, falls short in…
arXiv:2508.01681v2 Announce Type: replace Abstract: We study the regret minimization problem in the novel setting of generalized kernelized bandits (GKBs), where we optimize an unknown function $f^*$ belonging to a reproducing kernel Hilbert space (RKHS) having access to samples generated…
The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
arXiv:2512.10671v1 Announce Type: cross Abstract: Early-exit networks are effective solutions for reducing the overall energy consumption and latency of deep learning models by adjusting computation based on the complexity of input data. By incorporating intermediate exit branches into the architecture,…
arXiv:2508.02602v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to “inverse problems” to directly predict hidden…
arXiv:2512.09015v2 Announce Type: replace-cross Abstract: Frontier language model quality increasingly hinges on our ability to organize web-scale text corpora for training. Today’s dominant tools trade off speed and flexibility: lexical classifiers (e.g., FastText) are fast but limited to producing classification…
arXiv:2510.08169v3 Announce Type: replace Abstract: Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive…
arXiv:2411.14013v4 Announce Type: replace-cross Abstract: As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic speech and attributing it to its source model. Our method addresses three tasks:…
arXiv:2412.00173v2 Announce Type: replace Abstract: Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence…
