arXiv:2511.02625v2 Announce Type: replace-cross Abstract: We present an estimation of the condition numbers of the emph{mass} and emph{stiffness} matrices arising from shallow ReLU$^k$ neural networks defined on the unit sphere~$mathbb{S}^d$. In particular, when ${theta_j^*}_{j=1}^n subset mathbb{S}^d$ is emph{antipodally quasi-uniform}, the…
arXiv:2511.03928v1 Announce Type: new Abstract: We introduce and formalize the Synthetic Dataset Quality Estimation (SynQuE) problem: ranking synthetic datasets by their expected real-world task performance using only limited unannotated real data. This addresses a critical and open challenge where data…
arXiv:2511.04550v1 Announce Type: cross Abstract: The growth of cloud computing has revolutionized data processing and storage capacities to another levels of scalability and flexibility. But in the process, it has created a huge challenge of security, especially in terms of…
arXiv:2511.03929v1 Announce Type: new Abstract: We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reasoning tasks. Nemotron Nano V2 VL delivers significant improvements over our…
arXiv:2511.04622v1 Announce Type: cross Abstract: The Adam optimizer is currently presumably the most popular optimization method in deep learning. In this article we develop an ODE based method to study the Adam optimizer in a fast-slow scaling regime. For fixed…
arXiv:2511.03938v1 Announce Type: new Abstract: Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard “one prototype per class” design requires $O(CD)$ memory (with $C$ classes and dimensionality $D$). Prior compaction reduces $D$ (feature axis), improving storage/compute but…
arXiv:2407.03953v4 Announce Type: replace Abstract: Graph pre-training has been concentrated on graph-level tasks involving small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial…
arXiv:2511.03939v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard for aligning Large Language Models (LLMs), yet recent progress has moved beyond canonical text-based methods. This survey synthesizes the new frontier of alignment research by addressing…
arXiv:2411.06635v4 Announce Type: replace Abstract: Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains a major challenge. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL,…
arXiv:2511.03953v1 Announce Type: new Abstract: We address the problem of quickest change detection in Markov processes with unknown transition kernels. The key idea is to learn the conditional score $nabla_{mathbf{y}} log p(mathbf{y}|mathbf{x})$ directly from sample pairs $( mathbf{x},mathbf{y})$, where both…
