Deep Polynomial Neural Networks

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Real-world systems are shaped by both their complex internal interactions and the changes in their noisy environments. In this work, we study how a shared active bath affects the statistical dependencies between two interacting Brownian particles by evalua ...

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 ...

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 ...

Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus. Such regularizers give rise to variational denoisers that minimize a convex energy. They rely on few parameters (less than 15,000) and offer a signal ...

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 ...

The use of immersive representations for digital media has been gaining growing attention fueled by novel applications such as virtual and augmented reality. Point clouds, which represent shapes as a collection of unconnected points in space with geometric ...

Deep Polynomial Neural Networks

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

Automated Ensemble Multimodal Machine Learning for Healthcare

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

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

An integrative computational modeling approach for Drosophila motor control

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

Backpropagation-free Training of Analog AI Accelerators

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

Optimal Inference in Contextual Stochastic Block Models

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

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Information interference driven by environmental activity

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

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

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

SAMPa: Sharpness-aware Minimization Parallelized

On quality assessment and learning-based compression of point clouds for conventional and DNA-based storage