Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 advent of the "generative age"marks a pivotal moment in AI evolution, with models like OpenAI's GPT-4 and DALL-E transforming content creation across various fields. These advancements bring unprecedented efficiency and new applications but also presen ...

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 ...

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 ...

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 ...

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 ...

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 ...

Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires sequentially computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We ...

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 ...

Identifying manipulated regions in images is a challenging task due to the existence of very accurate image inpainting techniques leaving almost unnoticeable traces in tampered regions. These image inpainting methods can be used for multiple purposes (e.g. ...

Coded aperture cameras can be manufactured cheaply, have a very thin form-factor, and may be transparent and flexible, thus providing easy-to-integrate and compact image sensors. However, limitations in reconstructed image quality and resolution have imped ...

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

Chattez avec Graph Search

Automated Ensemble Multimodal Machine Learning for Healthcare

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

Label Critic: Design Data Before Models

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

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

Resource-efficient photonic networks for next-generation AI computing

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

Déjà Vu: Uncanny and Serendipitous Re-encountering of Audiovisual Archives

Backpropagation-free Training of Analog AI Accelerators

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

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

Optimal Inference in Contextual Stochastic Block Models

SAMPa: Sharpness-aware Minimization Parallelized

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

IRL-Net: Inpainted Region Localization Network via Spatial Attention

Toward High-Resolution Face Image Generation From Coded Aperture Camera