Publications associées à Data-Driven Kinematic Modeling of Physical Origami Robots

Publications associées (29)

FADE: Forecasting for anomaly detection on ECG

David Atienza Alonso, Denisa-Andreea Constantinescu

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Elsevier BV2025

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

Elise Hélène Dumas

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

Annual Reviews2025

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

Sophie Catherine Erb

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

EPFL2025

Multi-Objective Management for Many-Core Computing Servers

Darong Huang

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

EPFL2025

GraphOntology Dataset

Francisco Pereira Correia Pinto, Ramtin Yazdanian

The EPFL Graph Platform is an open-source data infrastructure designed for academic institutions. It organizes educational and institutional data into a semantically interconnected knowledge graph, making it accessible through a graph-based search engine a ...

Zenodo2025

Nonparametric network summaries

Arthur Verdeyme

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

EPFL2025

Cross-Net: Joint In-Line Holographic Image Reconstruction and Refocusing

Haitao Zhou

Holography enables quantitative phase sensing through light wave interference, allowing interesting imaging modalities applied in computational microscopy where phase shift is used as a way to generate image contrast. Recovering both amplitude and phase di ...

Springer Science and Business Media Deutschland GmbH2025

Editorial: Advances in robots for learning

Daniel Carnieto Tozadore

we were captivated by the opportunities and challenges of integrating robots into education, and collectively, we shared a deep enthusiasm for their potential to revolutionize social robotics in meaningful ways. ...

2025

Structure-based metabolite function prediction using graph neural networks

Tancredi Guido Cogne, Jacques Fellay, Ali Saadat Varnosfaderanii, Joanna Kathleen Hastings, Mariam Ait Oumelloul

Being able to broadly predict the function of novel metabolites based on their structures has applications in systems biology, environmental monitoring and drug discovery. To date, machine learning models aiming to predict functional characteristics of met ...

Oxford University Press (OUP)2025

Discovering meaningful units from text sequences

Melika Behjati

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

EPFL2024

Intraday solar irradiance forecasting using public cameras

Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini, Ilker Oguz, Roy Sarkis

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Pergamon-Elsevier Science Ltd2024

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Volkan Cevher, Fanghui Liu, Leello Tadesse Dadi

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

2024

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Devis Tuia

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

PUBLIC LIBRARY SCIENCE2024

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Ante Maric, Yiming Li, Sylvain Calinon

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Institute of Electrical and Electronics Engineers2024

SuperAnimal pretrained pose estimation models for behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Steffen Schneider, Jessy Lauer, Tian Qiu, Maxime Michel André Vidal, Anastasiia Filippova

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Nature Research2024

Towards Robust Vision Models

Harshitha Machiraju

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

EPFL2024

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Wenlong Liao

Supervised machine learning models are receiving increasing attention in electricity theft detection due to their high detection accuracy. However, their performance depends on a massive amount of labeled training data, which comes from time-consuming and ...

2024

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures

Ye Wei

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineer ...

2024

Euclid preparation LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods

Frédéric Courbin, Georges Meylan

Euclid will collect an enormous amount of data during the mission’s lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for comput ...

2024