Publications associées à EFaR 2023: Efficient Face Recognition Competition

Publications associées (19)

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

EPFL2025

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Luciano Loris Viteritti, Giuseppe Carleo

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

2025

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Xin Chen

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Wiley2025

TimeGPT in load forecasting: A large time series model perspective

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

2025

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Pierre Vandergheynst

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Nature Publishing Group2025

Enhanced Architectures and Optimization Methods for Efficient Language Modeling

Amirkeivan Mohtashami

This thesis investigates methods to construct more capable models more efficiently, focusing on two aspects: improved architectures and optimization. We examine principled architectural modifications that reduce computational costs or introduce features fo ...

EPFL2025

Teaching oxidation states to neural networks

Nicola Marzari

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with st ...

2025

Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label Classifier

Minh Hieu Lê

Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a patch-based classifier. This patch classifier is trained to identify classes p ...

Springer Science and Business Media Deutschland GmbH2025

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models

Philip Neil Garner, Louise Clothilde Coppieters De Gibson

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Ortal Yona Senouf, Ivan-Daniel Sievering

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Implicit phonetic information modeling for speech emotion recognition

Tilak Purohit

This study investigates the efficacy of utilizing embedding spaces to model phonetic information in emotion utterances for speech emotion recognition. Our approach involves implicit modeling of phone information by deriving phone-based embeddings from netw ...

Isca-Int Speech Communication Assoc2023

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Olga Fink, Gaëtan Michel Frusque, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Research Publishing2023

Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

Lijing Xin

IntroductionRecent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less ...

FRONTIERS MEDIA SA2023

Machine-learning accelerated identification of exfoliable two-dimensional materials

Kumar Varoon Agrawal, Giovanni Pizzi, Mohammad Tohidivahdat

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and effici ...

IOP Publishing Ltd2022

Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Mathieu Salzmann

Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. How ...

IEEE2021

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Lenka Zdeborová

In this paper we investigate how gradient-based algorithms such as gradient descent (GD), (multi-pass) stochastic GD, its persistent variant, and the Langevin algorithm navigate non-convex loss-landscapes and which of them is able to reach the best general ...

2021

E2CNN: Ensembles of Convolutional Neural Networks to Improve Robustness Against Memory Errors in Edge-Computing Devices

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Flavio Ponzina

To reduce energy consumption, it is possible to operate embedded systems at sub-nominal conditions (e.g., reduced voltage, limited eDRAM refresh rate) that can introduce bit errors in their memories. These errors can affect the stored values of CNN weights ...

2021

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

EPFL2020