AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
2,891 papers
The results show that Deflation-PINNs can successfully identify and characterize multiple distinct crystal structures: a single unsupervised run recovers all six stable states of the benchmark and the discovered branches are refined to percent-level accuracy.
The architecture decomposes multi-objective scheduling into a tripartite optimisation space managed by dedicated sub-agents for cost minimisation, anti-affinity fault tolerance, and vector resource balancing, and Agentic-Kube consistently achieves Pareto-efficient placements.
The results clearly demonstrate that FlowCorrect learns from very few demonstrations and enables fast, sample-efficient, incremental, human-in-the-loop corrections of generative visuomotor policies at deployment time in real-world robotics.
This work proposes Mine and Refine, a two-stage contrastive training framework that establishes a robust global embedding space via label-aware supervised contrastive learning, and substantially improves retrieval and end-to-end relevance metrics.
Reach audiences
Advertise in front of researchers, engineers, and readers.
This work uncover and identify the notion of ``robust item-wise coverage''as the minimal data requirement to enable sample-efficient robust assortment learning and bridges the gap between robustness and statistical efficiency in assortment learning.
The resulting learned policies consistently outperform standard heuristics and resist distillation into simple interpretable models, providing empirical evidence that deep reinforcement learning allows the agents to exploit non-linear geometric structure beyond the scope of traditional heuristics.
This work introduces prequential posteriors, based upon a predictive-sequential (prequential) loss function, and proves that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance.
Depression symptom severity was associated with five lexical features, including reductions in word count measures, use of first-person plural pronouns and positive word frequency, andLexical features and vector embeddings did improve prediction accuracy beyond baseline models.
This work develops GREAT, a novel framework for crafting natural distributional backdoors in RLHF, which targets harmful response generation for a vulnerable user subpopulation featured by semantically violent requests paired with emotionally angry triggers.
This work compares the performance of PINNs in solving inverse problems with that of a traditional approach using the finite element method combined with a numerical optimizer and finds that while PINNs may require less human effort and specialized knowledge, they are outperformed by the traditional approach.
This work introduces a probabilistic symbolic regression framework that represents mathematical expressions as ensembles of symbolic trees, and develops posterior concentration guarantees when symbolic expressions approximate the underlying relationship arbitrarily well, with a near-parametric rate when an exact finite formula exists.
Ampere is a novel collaborative training system that simultaneously minimizes on-device computation and device-server communication while improving model accuracy and reduces standard deviation of accuracy, highlighting superior performance when faced with heterogeneous data.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
