Self-Aware Social Learning Over Graphs

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The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stan ...

Synthetically grown high-quality single crystal diamond is an excellent base material for quantum photonics. Planar single crystal diamond substrates several square millimeters in size are nowadays commercially available. They can be precisely structured u ...

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

This article considers a network of agents interested in solving a classification t ask. The datasets available to accomplish the task are heterogeneous and dispersed across the agents. Each agent is inter-ested in discriminating among the 'inner' hypothes ...

Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition ...

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is st ...

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

Self-Aware Social Learning Over Graphs

Chat with Graph Search

Automated Ensemble Multimodal Machine Learning for Healthcare

Micro- and nanofabrication techniques for single crystal diamond photonics

Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

Non-asymptotic performance of social machine learning under limited data

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

DECENTRALIZED FUSION OF EXPERTS OVER NETWORKS

Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Optimal Inference in Contextual Stochastic Block Models

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

Backpropagation-free Training of Analog AI Accelerators

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees