Publications associées à Privacy-preserving federated neural network learning for disease-associated cell classification

Publications associées (23)

Machine learning enhanced atom probe tomography analysis

Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT dataset ...

2026

Personalizing Learning and Empowering Educators through Multi-Dimensional Clustering, Teacher Dashboards, and AI-Hybrid Learning Systems

Paola Mejia Domenzain

The shift from traditional classroom settings to technology-supported learning environments has led to the adoption of learning analytics and artificial intelligence (AI) in education. These technologies promise to support personalized learning by analyzin ...

EPFL2025

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

Florent Gérard Krzakala, Bruno Loureiro, Matteo Vilucchio

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...

2025

Hadamard product in deep learning: Introduction, Advances and Challenges

Volkan Cevher, Yongtao Wu

While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

2025

Robustly Learning Regions of Attraction From Fixed Data

Colin Neil Jones

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator represen ...

2025

synMARSS—An End-To-End Platform for the Parametric Generation of Synthetic In Vivo Magnetic Resonance Spectra

André Döring

Synthetic magnetic resonance spectra (MRS) are mathematically generated spectra which can be used to investigate the assumptions of data analysis strategies, optimize experimental design, and as training data for the development and validation of machine l ...

2025

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

Nikolaos Geroliminis, Robert Fonod

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo Internation ...

Zenodo2025

Neural networks trained with SGD learn distributions of increasing complexity

Maria Refinetti

The uncanny ability of over-parameterised neural networks to generalise well has been explained using various ‘simplicity biases’. These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning m ...

2025

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Thanh Trung Huynh

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...

2024

Function Learning with Financial Applications

Paolo Colusso

This thesis sets itself the goal of investigating function learning approaches. The topic is extremely relevant today, since the abundance of data, jointly with the technological progress which has enhanced computing power, now allow to represent functions ...

EPFL2024

Enhancing Explainability and Performance in AI-Driven Face Recognition and Deepfake Detection

Yuhang Lu

The rapid advancement of artificial intelligence (AI) has greatly influenced numerous research areas, leading to significant breakthroughs in human face-related technologies, particularly in face recognition and deepfake detection. While offering substanti ...

EPFL2024

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Berend Smit, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Kevin Maik Jablonka, Sauradeep Majumdar, Joren Jozef M. Van Herck, Beatriz Bueno Mouriño, Seyed Amirmohammad Elahi

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

ROYAL SOC CHEMISTRY2024

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Patrik Willi Hoffmann

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...

CELL PRESS2024

Truly No-Regret Learning in Constrained MDPs

Volkan Cevher

Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...

2024

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Mathieu Salzmann

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns bec ...

Association for Computing Machinery, Inc2024

Training Provably Robust Models by Polyhedral Envelope Regularization

Sabine Süsstrunk, Mathieu Salzmann, Chen Liu

Training certifiable neural networks enables us to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to obtain a provable adversarial-free region in the neighborhood of the input data by a polyhedr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Fast dynamic 1D simulation of divertor plasmas with neural PDE surrogates

Yoeri Renier Johannes Poels

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications ...

IOP Publishing Ltd2023

AMAE: Adaptation of Pre-trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert annotation of anomaly data. However, nearly all existing methods are formulated ...

Springer International Publishing Ag2023

The automated collapse data constructor technique and the data-driven methodology for seismic collapse risk assessment

Dimitrios Lignos, Alexandre Massoud Alahi, Nenad Bijelic

Majority of the past research on application of machine learning (ML) in earthquake engineering focused on contrasting the predictive performance of different ML algorithms. In contrast, the emphasis of this paper is on the use of data to boost the predict ...

WILEY2023

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model

Jean-Philippe Thiran

Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted diagnostic solution. Here, we propose a deep learning-based workflow for the evaluation of CRC lymph node me ...

ELSEVIER SCIENCE INC2023