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1,737 papers
Generative adversarial networks (GANs) dominate dataset augmentation work, graph-based communication analysis addresses dependency modelling, and methods based on transformers or federated learning emerge from 2023 onward.
The evolution of healthcare into a data-centric, intelligent, and connected field has resulted in better diagnostic precision, targeted treatments, remote monitoring, and operational effectiveness. But the evolution also makes sensitive medical information and vital health operations vulnerable to cybersecurity threats...
Viral hepatitis remains one of the most significant global public health challenges, affecting more than 300 million individuals worldwide and causing approximately 1.3 million deaths annually due to cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite remarkable advances in vaccination strategies, hig...
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This repository contains the runnable implementation and experiment scripts accompanying the paper "Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning". It implements a lattice-trapdoor identity-tracing mechanism for federated prototype learning (FedLHT) combi...
Fog computing positions computation and storage resources in close proximity to data sources, promisingmillisecond-scale responsiveness and data handling that the distant cloud cannot deliver. Over the pastdecade, researchers have proposed a constellation of mechanisms such as virtualisation, latency-awareservice place...
Despite major medical breakthroughs, Hepatitis B (HBV) and Hepatitis C (HCV) continue to silent-kill millions globally, primarily because they go undetected until severe liver damage sets in. While direct-acting antivirals and suppressive therapies work wonders, our screening methods are lagging behind. We still rely o...
Federated learning combined with differential privacy offers a practical way to train image-analysis models on distributed camera and sensor networks. This combination is most often implemented through DP-SGD, and it avoids centralizing raw visual data. This mini review examines differentially private federated learnin...
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...
This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset and found that glucose, BMI, diabetes pedigree function, and age were the dominant predictors.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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