AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
Quantized ABBA (QABBA), a quantized version of ABBA, provides an error-controlled, low-precision symbolic representation for time-series compression and LLM-based analysis.
This paper forms the resulting $\ell_0$-constrained empirical newsvendor problem with $\ell_2$-regularization, establishes its computational hardness, and develops a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation.
This work establishes generative augmentation as a distributional perturbation process whose reliability can be quantified through Wasserstein-based measures and supported by finite-sample generalization guarantees.
This work addresses the inverse problem of matched score queries at noise scales $\sigma$ and $\lambda\sigma$ using matched subtraction to establish a perturbation bound and a conditional kernel-density-estimation rate.
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This paper formalizes a mathematical framework for the Artificial Rosetta Stone, proposing the Artificial Rosetta Stone (ARS), and separates three claims often conflated: a symbolic sequence can be reconstructed probabilistically; a sequence can be consistent with an explicit grammar; and a historical performance can b...
Numerical studies and a semi-synthetic analysis based on hard-drive failure data illustrate potential reductions in expected error rate and improvements in decision-boundary estimation from modelling feature-dependent label missingness.
A general convolutional neural network framework for detecting heterogeneous event-driven dynamics in univariate time series windows and it is shown that the induced CNN class exactly represents classifiers based on range, maximum drawup, maximum drawdown and slope change, and uniformly approximates realised volatility...
This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional target densities and offers preliminary evidence that the established scaling behavior of standard diffusion training transfers directly to exact sampling from high-dimensi...
It is shown that one-layer transformers with an argmax classification head behave identically to a one-nearest-neighbor classifier in the multiclass setting by leveraging the simplex encoding.
It is proved that a multi-head scaled dot product attention can be viewed as a parameter identification strategy, and a purely mathematical/statistical explanation, identification, for why specific architectural choices in transformers may have improved performance is discussed.
A perturbative regime in which the central flow is the limit of gradient descent, and the central flow emerges as the leading-order term in the expansion, while the self-stabilization mechanism appears in the next-order term.
This work introduces Pref-MH, a general exact MH sampler for judge-induced conditional distributions using only stochastic binary pairwise comparisons, and develops a valid accept/reject rule whose resulting Markov chain provably converges to the target distribution.
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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