AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,572 papers
Bug-fix release. No options were added or removed. The thirty fixes below came out of a coverage push that took the test suite from 311 to 718 tests and line coverage from 68 to 95 percent, and out of the bug reports filed along the way. Several fixes change reported numbers: a multi-allelic codon now counts each of it...
The anatomical basis, causes, pathophysiology, clinical manifestations, diagnostic evaluation, treatment approaches, complications, and prognosis of uncal herniation are reviewed.
The proliferation of Internet of Things (IoT) devices and latency-sensitive applications has increased the need for edge computing, which complements existing cloud systems by enabling data processing closer to the source. By processing data closer to the source, edge computing reduces latency, bandwidth consumption, a...
This review examines Green AI as an emerging paradigm for developing energy-efficient machine learning systems and explores its intersection with the principles of responsible AI, providing a roadmap for researchers and practitioners seeking to develop AI systems that balance computational performance with environmenta...
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Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence sm...
The rapid advancement of big data analytics is beginning to significantly impact the progress of medical research and practice, enabling data-driven decision-making and innovation. It has led to the creation of tools for collecting, organizing, processing, and integrating vast amounts of diverse, structured, and unstru...
This repository provides the source code, configuration files, synthetic datasets, and experimental results for the paper entitled "Semantic Memory-Regularized Uncertainty-Aware Meta-Reinforcement Learning for Adaptive Task Scheduling in 6G Edge Intelligence". The repository implements a semantic memory-regularized unc...
This repository provides the source code, configuration files, synthetic datasets, and experimental results for the paper entitled "Semantic Memory-Regularized Uncertainty-Aware Meta-Reinforcement Learning for Adaptive Task Scheduling in 6G Edge Intelligence". The repository implements a semantic memory-regularized unc...
Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...
Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22693218. Does the introduction explain the objective of the research presented in the preprint? Yes The introduction states its objective directly rather than making the...
The swift advancement of multimodal artificial intelligence (AI) has changed the face of integration of diverse data types, such as medical images, texts, biosignals, genomics, wearables, and clinical data in the field of medicine. However, the deployment of reliable, secure, and scaled multimodal healthcare systems is...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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