AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
This approach provides a unified framework to handle MLP within both classical and quantum LLMs and shows that the Pythagorean inequality continues to hold in the infinite-dimensional setting whenever the convex information-projection problem attains a finite minimum.
This work proposes a modular and provably accurate cut-Bayes approach requiring no access to upstream data or model, and establishes, to the knowledge, first results on uniform Kullback-Leibler approximation rates of conditional distributions by common flow classes, yielding widely applicable fixed-data error rates for...
DLM-One eliminates iterative refinement by aligning the scores of a student model's outputs with the score function of a pretrained teacher DLM in the forward-diffused noisy space, which positions one-step score distillation as a viable path for the efficient deployment of continuous diffusion models operating in conti...
Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dea...
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Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $\alpha$-TS, where we use a fractional or $\alpha$-posterior ($\alpha\in(0,1)$) instead of the standard posterior distribution. To compute an $\alpha$-posterior,...
This work analyzes the convergence of the noisy power method and Oja's algorithm, both studied for the stationary data generating model, and argues that the noisy power method is rate-optimal in this setting.
Canon, a risk-averse method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems, is introduced and Cantelli's inequality is employed to obtain a tractable, conservative and smooth bound on the VaR constraint based on the first two moments of the cost return.
A novel sampling algorithm is presented that uses observed label frequencies to estimate multivariate Bernoulli distribution parameters and calculates weights for each label combination, to preserve category frequency order, reduce frequency differences between most and least common categories, and account for category...
On a 124M-parameter language model and selected short training windows, the method can lower simulated ledger cost relative to the tested baselines when fleet work is sufficiently cheap, and can lower simulated ledger cost relative to the tested baselines when fleet work is sufficiently cheap.
The anytime lower bound shows that the rate of non-anytime silver schedules (Altschuler and Parrilo, 2025; Grimmer et al., 2025) is unattainable in the anytime setting, and establishes a strict separation between the two settings.
It is proved that in noiseless Gaussian compressed sensing, the full-dimensional linear prior attains the minimum expected reconstruction error over the entire family of linear priors, indicating that the experimental benefits of tunability in compressed sensing with neural network priors arises due to nonlinearities i...
A three-regime batch-size phase diagram that reveals how the role of momentum changes with batch size is revealed, with Polyak enlarging the critical batch size, the largest batch size preserving the best small-batch data-scaling exponent, thereby enabling greater parallelism without sacrificing data efficiency.
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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