Publications associées à CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction

Publications associées (22)

Advancing understanding and practical performance of machine learning interatomic potentials

Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

EPFL2025

Practical Federated Learning without a Server

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Akash Balasaheb Dhasade, Erick Lavoie

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

ACM2025

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

Andrea Martin

Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

EPFL2025

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

Rui Yao

Deep neural networks (DNNs) have been increasingly applied in travel demand modeling because of their automatic feature learning, high predictive performance, and economic interpretability. Nevertheless, DNNs frequently present behaviorally irregular patte ...

PERGAMON-ELSEVIER SCIENCE LTD2024

GELEX: Generative AI-Hybrid System for Example-Based Learning

Aybars Yazici, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Jibril Albachir Frej

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Association for Computing Machinery2024

Feature Representations for Automatic Meerkat Vocalization Classification

Mathew Magimai Doss, Eklavya Sarkar

Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations of other social animals such as, meerkats, marmosets, apes. While existing ap ...

2024

On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach

Nicolaj Andreas Bösel-Schmid, Karim Samaha, Etienne Pierre Chassaing, Colin Neil Jones, Roland Schwan

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, a ...

Elsevier B.V.2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Hierarchical Federated Learning in MEC Networks with Knowledge Distillation

Thanh Trung Huynh

Modern automobiles are equipped with advanced computing capabilities, allowing them to become powerful computing units capable of processing a large amount of data and training machine learning models. However, machine learning algorithms typically require ...

Institute of Electrical and Electronics Engineers Inc.2024

Automatic quantification of scapular and glenoid morphology from CT scans using deep learning

Alexandre Terrier, Pezhman Eghbalishamsabadi

Objectives: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis. Materials and Methods: First, we used dee ...

2024

Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack

Hatef Otroshi Shahreza

Face recognition systems use the templates (extracted from users' face images) stored in the system's database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images re ...

Ieee-Inst Electrical Electronics Engineers Inc2024

ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots

Auke Ijspeert, Guillaume Denis Antoine Bellegarda, Milad Shafiee Ashtiani

Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must b ...

Institute of Electrical and Electronics Engineers Inc.2024

Overflow-free Compute Memories for Edge AI Acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

Assoc Computing Machinery2023

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

François Maréchal, Xiang Li

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and ma ...

ELSEVIER2023

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

IEEE2022

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Emmanuel Abbé

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

ASSOC COMPUTING MACHINERY2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Sadegh Farhadkhani, John Stephan

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

PMLR2022

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Michael Christoph Gastpar

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve informatio ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs

Michael Christoph Gastpar, Jan Hazla, Ido Nachum

How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stat ...

2022