AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,822 papers
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial progress in RandNLA-based algorithms, few implementations combine GPU acceleration with a simple interface for specifying optimization prob...
InfluenceField, an intervention-aware latent field inserted between the visual encoder and language decoder, is introduced, an intervention-aware latent field inserted between the visual encoder and language decoder that lifts patch features into a continuous spatial representation, propagates directed influence over m...
Comparisons of statistical and machine-learning-based methods for post-processing ECMWF 2-m temperature and 10-m wind speed forecasts at observed and unobserved stations in Germany consider EMOS-based approaches, distributional regression networks, Transformers, and graph neural networks under both limited and extended...
Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused multiply-accumulate (FMA) operations and memory traffic. To reduce these computations while retaining dense tensors for high GPU throughput, we develop a methodology from f...
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This paper presents the first polynomial-time algorithm for learning with exact group invariances that applies uniformly to finite and infinite groups, and shows that exact symmetries can be identified from data and exploited for learning in polynomial time.
An exact distance identity for two coupled updates is derived and approximate non-expansiveness of the common-example map is proved, with a quadratic defect only when the two latent margins straddle zero.
This work introduces ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights, establishing a scalable, regulation-aligned pathway for continual, privacy-conscious learning.
This work introduces Selective Posterior Margin Regularization (SPMR), which preserves the Forward objective and converts this disagreement into a graded update on the clean classifier and transfers to estimated transitions, human annotations, architectural changes, and stronger Forward recipes.
This work studies adaptive routing of prompts to large language model experts to maximize response quality in an online setting with limited feedback and proposes algorithms that strategically select and observe rewards to minimize regret.
We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statistical guarantees of score matching from the loss geometry and optimization signal at a fixed diffusion noise level. The KL divergence links the denoising score matching o...
This paper applies the A4/LEARN PET archive, treating observed PET as a design laboratory for scarce-confirmation studies and asks a deliberately operational question: when is simple transparent PET validation enough, and when is a fitted residual-uncertainty score worth the added complexity?
LLM-guided search is usually adopted to solve complex tasks by ranking and pruning top-$K$ candidates based on evaluator scores. However, irreducible bias still exists even if popular methods, such as repeated sampling, are applied to reduce variance. Consequently, pruning may remove every continuation that can reach a...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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