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

Related publications (24)

Automated Ensemble Multimodal Machine Learning for Healthcare

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 ...

2025

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

Devavrat Tomar

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 ...

EPFL2025

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

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 ...

2025

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

Stefan Christian Peters, Martin Vetterli, Eric Bezzam

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 ...

IEEE2025

Accelerating MoE Model Inference with Expert Sharding

André Loureiro Espírito Santo, Anne-Marie Kermarrec, Rafael Pereira Pires, Marinus Abraham de Vos, Milos Vujasinovic

Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communication overhead. While prior work has focused on optimizing MoE training and d ...

Association for Computing Machinery2025

Optimal Inference in Contextual Stochastic Block Models

Lenka Zdeborová, Odilon Marie Cyprien Duranthon

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 ...

Journal of Machine Learning Research Inc.2024

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

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Thomas Yu

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 ...

IEEE Computer Society2024

Backpropagation-free Training of Analog AI Accelerators

Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac

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 ...

Institute of Electrical and Electronics Engineers Inc.2024

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Rachid Guerraoui, John Stephan

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 ...

Association for Computing Machinery, Inc2024

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Sandro Carrara, Francesca Rodino, Lin Du

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 ...

Pergamon-Elsevier Science Ltd2024

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

Arthur Barakat

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 ...

EPFL2024

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Yoeri Renier Johannes Poels

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles ...

2024

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students

Seyed Parsa Neshaei

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

Association for Computing Machinery2024

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

Neha Tarigopula

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 ...

IEEE2024

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

Csaba Zsolnai, Henry Markram, Daniel Keller, Olivier Burri, Rodrigo de Campos Perin, Ying Shi, Julie Meystre, João Luís Prado Vieira, Jean Nicolas Jacquemier

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 ...

2024

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Daniel Gatica-Perez, Sina Sajadmanesh

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 ...

Assoc Computing Machinery2024

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

Jozef Marus Coldenhoff

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 ...

IEEE2024

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

Karla Montserrat Castro Gilabert

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 ...

EPFL2024

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Marta Knezevic, Tanja Christina Käser Jacober, Jibril Albachir Frej, Tatjana Nazaretsky

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 ...

Assoc Computing Machinery2024

SAMPa: Sharpness-aware Minimization Parallelized

Volkan Cevher, Thomas Michaelsen Pethick, Wanyun Xie

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 ...

2024