AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
Moral Entropy is introduced, a Bayesian framework that keeps a full posterior over the true label and decomposes its entropy into aleatoric uncertainty (irreducible disagreement about the moral content) and epistemic uncertainty (from insufficient or noisy annotation) -- and lets any heuristic consensus rule be audited...
This work introduces ISOMORPH, the first public digital twin of a multi-echelon logistics network with interpretable, user-configurable parameters and modular topology, demand, and control rules, and introduces forecasting regimes that differ from standard real-world TSF benchmarks.
This work proposes an amortized framework for filtering and smoothing that reuses trained conditional models across observation sequences and assimilation times and shows that, under the Markov assumption, a summary sufficient for filtering is also sufficient for the backward kernel.
Despite the ubiquity of multiway data across scientific domains, there are few performant and user-friendly methods that fit non-standard nonnegative tensor factorization models tailored to the data at-hand. Researchers may use gradient-based automatic differentiation, which often struggles under nonnegative constraint...
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This work develops Boltzmann Generators tailored to amorphous materials by building the required equivariances directly into Riemannian stochastic interpolants and incorporates periodic boundary conditions and particle symmetries using equivariant graph neural networks.
Machine learning models optimized for average performance can perform poorly on vulnerable subpopulations. Existing approaches often rely on groups specified in advance, yet fairness-relevant subgroup structure may be latent, intersectional, and driven by complex interactions among continuous and discrete attributes. W...
This paper considers NMF using the component-wise L1 norm as the error measure (L1-NMF), which is suited for data corrupted by heavy-tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers, and proposes a new coordinate descent approach for wL1-NMF, denoted as sparse CD (sCD), where...
Fidel-TS is introduced, a new large-scale benchmark built from the core principles of high-fidelity benchmarking, focusing on data sourcing integrity, leak-free design, and structural clarity, to address the evaluation of time series forecasting models.
This work introduces a novel inductive bias towards simple policies in reinforcement learning by minimizing the entropy of entire action trajectories, corresponding to the number of bits required to describe information in action trajectories after the agent observes state trajectories.
This work establishes an exact integral representation of $\varepsilon_\text{fact}$ separating the schedule from the target's dependence structure, summarized by a dependence density $\rho$, which yields recursive stationarity equations for optimal schedules and allows us to quantify how estimation errors in $\rho$ aff...
We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary. Because responses at different points lie in different tangent spaces, the proposed kernel estimator first parallel transports nearby responses to the target tangent space and then forms a volume-corrected local average...
These OS guarantees match the best-known bounds achieved by multi-loop methods while preserving a single-loop implementation, and provide the best-known SFO complexity guarantees among single-loop stochastic first-order methods for the respective stationarity criteria and problem classes.
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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