AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
A CTD approach that decouples convergence and diversity into two stages, aiming to quickly drive the search toward a single point on the Pareto front and focuses on diversity, aiming to spread solutions across the front.
This work provides a conceptual foundation for governance-aware, adaptable, and AI-first software engineering methods by proposing a metamodel that treats autonomy not as a fixed property of an actor, but as a derived, situation-dependent authority assignment determined by task, context, and collaboration pattern.
Representation-based Masked Diffusion Model (RMDM) is proposed, a framework that leverages the text representation to explicitly encode global semantics and help to parallel update tokens more precisely, and significantly improves generation quality, particularly in aggressive few-step sampling regimes.
Borders reveal a noise--drift tradeoff: in noise-dominated regimes, first-moment averaging and adaptive preconditioning can yield favorable upper guarantees, whereas in drift-dominated regimes, stale first-moment information and preconditioning can enlarge Adam's tracking guarantee, potentially allowing vanilla SGD to...
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Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic oversampling can reduce imbalance, but discrepancies between the synthetic and target minority distributions may bias the fitted classifier, especially because synthetic samples...
Mixed-effects models are widely used to model data with complex grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed random effects, current standard computations relying on Cholesky decompositions can become prohibitively slow. In this work, we present Krylov...
This work achieves the first local convergence algorithm for self-selection by providing the first local convergence algorithm for self-selection, thus resolving one of the main open questions of Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis.
This article proposes a novel variational framework based on tangent transformation for a broad class of probability models characterized by strongly super-Gaussian likelihoods, thereby inducing conjugacy with Gaussian priors over model parameters in an otherwise intractable setup.
This work demonstrates how statistical methodology can be used for quantifying uncertainty in metrics that have been aggregated across multiple tasks, and emphasizes bootstrapping, Bayesian hierarchical (i.e., multilevel) modeling, and the visualization of task weightings that consider standard errors.
Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing regret minimization in bandit optimization. In this setting, the learner aims at selecting satisficing arms (arms with mean reward exceeding a certain threshold value) as frequently as possible. The performance is measure...
It is shown that the Transformer -- a cornerstone of modern sequence modeling -- can be constructed using hierarchical local models, revealing the ability of the localization method to unify and generalize state-of-the-art architectures.
It is shown that while observational outcomes can provide valuable supervision, directly fine-tuning models on such data can lead them to learn spurious correlations, and proposes DeconfoundLM, a method that explicitly removes the effect of known confounders from reward signals.
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
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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