AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
The proof combines two deterministic obstructions: a stationary small-gap instance that makes repeated forgetting costly, and a one-breakpoint instance that creates a strict post-change index lock that yields an algorithmic separation.
Graph Neural Simulators (GNS) is proposed as a principled surrogate modeling paradigm for time-dependent PDEs that mimics traditional numerical solvers, enabling stable long-horizon rollouts and strong inductive biases that enhance generalization.
The present work provides the first full exposition of QCML as a framework for quantum geometry, establishing its mathematical foundation and demonstrating how operator-level tools from quantum geometry can be directly applied to data.
Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed labels in multilabel classification. Conformal risk control (CRC; Angelopoulos et al., arXiv:2208.02814) gives distribution-free guarantees for such losses, but it calib...
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Latent Inference-Time Guidance for TSFMs is introduced, which adaptively combines a pool of TSFM forecasts through a time-dependent latent space with independent components that comes equipped with identifiability and reconstruction guarantees, whilst maintaining the off-the-shelf aspect of foundation models.
The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly characterize the error of downstream decisions. For a binary hypothesis $Y$ inferred from many separately encoded observations, the optimal error exponent is the Chernoff...
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions th...
PADI (Post-Anomaly Detection Inference), a novel framework that equips a trained and frozen Deep SVDD detector with statistically valid inference by leveraging the Selective Inference framework, is proposed, providing a principled approach for statistically reliable inference in semi-supervised anomaly detection settin...
Many datasets carry an intrinsic directionality: citations point backward in time, cells differentiate along lineages, and traffic follows preferred routes. Spectral embedding methods, including most of their extensions to directed graphs, discard this information: they symmetrize the data and map it into a Euclidean s...
Bre\v{s}ar and Mijatovi\'c \cite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude superlinear drift. We instead test superlinear Langevin diffusions for score-based image generation, computing their conditional scores numerically from a Fokker--Pl...
Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning. We study regression problems in which the test distribution differs from the training distribution and ask: under what assumptions on the target function or operator is stable extrapolation possible, and how far beyond the t...
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream decisions while controlling computational and sample requirements. At the same time, some multiclass problems have meaningful label hierarchie...
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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