AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,791 papers
A statistical approach to bias rectification that can be combined in a turn-key fashion with any classical GZSL feature learner as a blackbox, thereby suggesting a wide scope of applicability of this method for a wide variety of GZSL implementations in different domains.
This work introduces FlowCPO, an offline forward-KL objective that uses both preferred and dispreferred samples without online rollouts and shows under explicit regularity conditions that the forward-KL objective is bounded by a contrastive flow matching loss, yielding a tractable surrogate on fixed data.
An exact finite-sample necessary-and-sufficient condition for two-stage training to strictly outperform real-only training under the same real-data budget and identical real-stage updates is established.
This work characterize the asymmetry between active and inactive interference and the trade-off between interference and observation-noise budgets and introduces IHT-SAE, which uses learned iterative refinement to improve feature recovery beyond the limits of linear availability.
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This work gives an exact, conservative one-coordinate finite-task confidence certificate and a reporting standard separating direct estimates, identified sets, and model-conditioned forecasts, and gives exact count-law-preserving constructions with incompatible extrapolations with incompatible extrapolations.
The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on higher input derivatives. For smooth scalar-input fully connected networks, using a joint Gaussianity of the finite derivative...
<!-- release-title: v1.10.0: MCP --> wisp-science v1.10.0 v1.10.0 brings scientific MCP tools into Wisp's native Rust runtime. The bundled catalog provides 23 connectors and 247 tools across roughly 80 upstream databases, with browsable connector introductions and tool parameters. This release also improves first-time...
Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable index algorithms, including UCB1 and its generalizations, and derive sharp leading-order expressions for the sample-mean bias and expected $Z$-statistic. Our characterization...
Learning operators from sequentially collected data arises in adaptive experimental design, Bayesian optimization, and dynamical-system modelling, where observations may be dependent, and future inputs or sensing operators may depend on preceding data. We derive time-uniform self-normalized concentration bounds for sto...
An additive deep-learning framework that keeps physicochemical descriptors and molecular graph information separate throughout training attains competitive predictive performance while making the distinct roles of chemical and structural information more transparent.
This work proves that the representation cost is a power of a quasi-seminorm and that the native space is a quasi-Banach space with nonconvex unit ball when $L>2$.
We study the contradiction graphs associated with a binary concept class. For a class $H\subseteq\{0,1\}^X$, the order-$m$ contradiction graph $G_m(H)$ has as vertices the $H$-realizable labeled sequences of length $m$, with two vertices adjacent when the two sequences assign opposite labels to some common domain point...
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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