Automated Ensemble Multimodal Machine Learning for Healthcare

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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 ...

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 ...

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that p ...

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interfe ...

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural ...

Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics' unique st ...

Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the critical need for innovative urban development solutions. In response, sustainable smart cities are increasingly turning to cutting-edge technologies—su ...

Strong gravitational lensing has become one of the main astrophysical probes of the extragalactic universe. It allows precise measurements of deflector galaxy masses. It acts as a cosmic telescope, revealing sources otherwise too dim or small to study. Whe ...

Animal motor control arises from the intricate interplay between neural circuits, biomechanics, and sensory feedback. Deciphering how these components interact is essential for uncovering the fundamental principles of movement generation. This dissertation ...

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 ...

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 ...

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 ...

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 ...

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

Sign language is vital for communication within the deaf and hard-of-hearing community. Avatar-based methods and deep learning techniques like Generative Adversarial Networks have shown promise in generating sign language video content. One of the challeng ...

Proteins are macromolecular machines central to virtually every fundamental biological function. Three-dimensional protein structures, determined by their amino acid composition, are directly linked to their function. Research into structure-function relat ...

Automated Ensemble Multimodal Machine Learning for Healthcare

Chattez avec Graph Search

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

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

Label Critic: Design Data Before Models

Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Resource-efficient photonic networks for next-generation AI computing

Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin

Finding gravitational lenses at the dawn of the new generation of wide-field surveys

An integrative computational modeling approach for Drosophila motor control

Backpropagation-free Training of Analog AI Accelerators

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

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

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

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

Multi-motif scaffolding for de novo pathogen antigen mimetics by deep generative learning