AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
3,822 papers
We determine the sharp restricted isometry threshold for recovery at global minima of the rank-restricted matrix LASSO. For target rank $r_{\star}$, if the rank-$k$ RIP constant satisfies $\delta<\delta_{\mathrm{sharp}}(k/r_{\star})$, where $\delta_{\mathrm{sharp}}(t)=t/(4-t)$ for $0<t<4/3$ and $\delta_{\mathrm{sharp}}...
This work proposes Jigsaw-CRL, a framework for recovering global latent causal order from a fragmented multi-client setting, where multiple clients interact with the same global latent causal system but each client only accesses and intervenes on a subset of the latent variables.
The aggregate IGC mass admits bounds in terms of total correlation and dual total correlation, thereby connecting the pathwise geometry to classical measures of multivariate dependence and connecting the pathwise geometry to classical measures of multivariate dependence.
This work establishes quantum signal processing (QSP) as a solvable quantum model of the representation-learning regime, and proves a sparse-data guarantee for the full nonlinear gradient flow without freezing or ensemble-averaging the kernel.
Reach audiences
Advertise in front of researchers, engineers, and readers.
We establish quantitative convergence to the target and uniform-in-time propagation of chaos for Langevin-regularized Stein variational gradient descent. The Stein interaction need not be small relative to the confining Langevin drift and does not generally yield a contractive particle coupling. At the mean-field level...
The result shows that even a fixed-dimensional latent space suffices to achieve vanishing approximation error as the network budget increases, and it is proved that the optimal worst-case uniform approximation error over the unit ball ofolder functions on $[0,1]^d$ has the sharp order.
This work introduces Selection-Aware Semantic Stress Testing (\SASST{}), which learns a task reweighting from pre-execution features on discovery tasks and evaluates the same paired comparison on separate confirmation tasks.
Anytime-valid methods proposed for dependent data should be accompanied by null-calibration controls and mechanism traces, and Huber-style gating of the filter's own updates cuts isolated-spike degradation by an order of magnitude with no dataset specific tuning.
This work conducts a systematic, partially crossed evaluation of five offline RL algorithm families and 14 reward designs in 44,894 post-2018 acute ischemic stroke patients from a nationwide registry, and provides an empirically motivated six-step evaluation checklist.
This paper proposes a trainable Neural Cellular Automata (NCA) based surrogate model for learning long time PDE dynamics that achieves the lowest long-horizon relative errors on the majority of the experiments.
CAS (causal active sequential experimentation), which targets evaluation to model-workload pairs and repeats the test as evidence accumulates, to ask whether one assignment stays optimal across every quality table consistent with the evidence.
Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform repla...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
