Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

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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 ...

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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 ...

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 ...

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 ...

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 ...

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device select ...

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

Chattez avec Graph Search

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

Automated Ensemble Multimodal Machine Learning for Healthcare

Non-asymptotic performance of social machine learning under limited data

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

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

Development of performance portable spline solver for exa-scale plasma turbulence simulation

Optimal Inference in Contextual Stochastic Block Models

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

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

Backpropagation-free Training of Analog AI Accelerators

Multi-Channel Mosra: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and A Teacher Model