AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
A framework that extends standard single-objective acquisition functions directly to the multi-objective setting through a hypervolume-based transformation and extends hedge strategies for acquisition functions, which are typically used only in single-objective optimization, to the multi-objective regime is proposed.
A theorem-level pipeline in symmetry-quotient settings is developed: quotient linear stability implies quotient flatness, quotient flatness implies input smoothness, and input smoothness yields generalization under local covering assumptions.
Motivated by local sleep, the use-dependent off periods of individual cortical circuits in awake animals, it is shown that replay can instead be written into the degrees of freedom the current input leaves unused.
This paper revisit the nuances of long-term fairness achievable by an ADM-- specifically, through the lens of a loan approver inducing a population-level wealth dynamics, by developing Eutopia: a lending-process simulator enabled with a novel performative data generator to learn long-term fair strategies.
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SF-NorMuon, a schedule-free spectral optimizer that closes the performance gap with tuned AdamW baselines, makes horizon-free optimization more practical, taking a step towards truly open-ended, continual learning.
BGM-IV, a Bayesian generative modeling approach that performs nonlinear IV regression through posterior inference in a causally structured latent space, outperforms existing nonlinear IV methods overall, with significant gains in high-dimensional settings, while achieving near-nominal predictive coverage.
The proposed alignment method is statistically consistent and yields significantly tighter convergence guarantees than DDRO, and is based on the relative density ratio between the preferred data distribution and a mixture of the preferred and non-preferred data distributions.
A soft regression forest decision rule is proposed that preserves the interpretability of classical decision trees while being fully parametric, differentiable, and Lipschitz-smooth, enabling intrinsic interpretation from both global and local perspectives.
Compared to both unstructured baselines and strictly equivariant models, the soft Clifford transformer achieves better long-horizon fidelity, particularly in regimes with broken symmetries, suggesting that geometric algebra offers an effective middle ground.
As demands for resource efficiency and safety in modern neural networks intensify, substantial research effort has gone into model compression and adversarial robustness. Yet despite progress on each in isolation, a systematic understanding of how compressibility shapes robustness remains elusive. In this paper, we dev...
This work identifies and characterize an interesting dichotomy in the rates achievable in the supervised setting: there is a weak transfer regime where optimal rates are determined by information in either the source or target dataset but not both, and a strong transfer regime where much faster target rates are achieva...
Solomonoff induction is relativized to an information vantage point, biasing toward short programs with access to all preexisting information, which reframes the inductive bias: instead of seeking some absolute notion of simplicity, it favor accessibility with respect to the authors' vantage point.
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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