AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
A reconstruction-driven design density is derived and the posterior mean is used to build a training-free warp that places more measurements where the target function varies rapidly, and a geometric equalizer separately corrects boundary bias.
It is proved that the relationship between SPR and calibrated forecasting is bidirectional: not only do lower bounds for SPR translate into lower bounds for calibration, but algorithms for SPR also translate into new algorithms for calibrated forecasting.
The Sparse Landmark Embedding (SLE) kernel is proposed, and it is demonstrated, using geodesic and Wasserstein distances, that the SLE kernel matches or substantially exceeds domain-specific baselines in both predictive accuracy and uncertainty quantification.
This work considers the setting where $D$ is a linear differential operator and analyzes a physics-informed kernel estimator combining value observations and differential observations to derive physically consistent rates in a stronger norm that jointly controls the errors in $\hat u$ and $D\hat u$.
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This paper derives explicit Wasserstein stability bounds that quantify the effect of relative graph perturbations on the generated distributions and introduces a principled framework for designing stable graph filters that preserve the smoothing behavior of graph heat diffusion, while boosting structural stability.
This paper provides a self-contained proof of the rank of the pathlifting Jacobian of a DAG ReLU network by performing an induction on the network's number of hidden nodes by considering the skeleton matrix of the network, a sparse matrix encoding the network paths, and transform the representation of one of its hidden...
This paper introduces the technique of "persistence of memory" to greatly extend and improve the random subspace methods, and establishes to the knowledge the first theoretical analysis of classical SSD methods for sparse functions.
We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation constant. By combining Wasserstein transport with Fisher-Rao birth-death dynamics, WFR flows balance exploration and selection. These flows have been recognised as a promising...
A new single-loop algorithmic framework for smooth nonconvex--concave minimax optimization and a lower bound of $\Omega(L^2D_Y\Delta_\phi\varepsilon^{-3})$ for optimization stationarity over projected zero-respecting first-order methods is established.
Manifold-Based Clustering (MBC) is proposed, which returns an explicit bracket interval to quantify the underlying data uncertainty, and suggests that ambiguity in cluster number is often intrinsic, and should be quantified rather than resolved.
It is proved that a family of such deletion sets of cardinality \(q\) has size at most \(\exp(8k+2q)\).
METALICA, which implements Metadynamics on a pretrained diffusion model via Replica Exchange, is introduced, which implements Metadynamics on a pretrained diffusion model via Replica Exchange and accumulates a bias potential along a Collective Variable, repels new samples from previous ones through biased sampling, and...
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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