Publications associées à Divergences in color perception between deep neural networks and humans

Publications associées (28)

Dependable learning and control through Neural ODEs

Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

EPFL2025

Controlled learning of pointwise nonlinearities in neural-network-like architectures

Michaël Unser, Alexis Marie Frederic Goujon, Stanislas Ducotterd

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

2025

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

International Conference on Learning Representations, ICLR2025

Texture Synthesis using Neural Cellular Automata

Ehsan Pajouheshgar

Self-organization and emergent behavior lie at the core of many natural phenomena, from pattern formation in biological tissues to the collective motion of flocking birds. Harnessing these principles in computational models can unlock powerful approaches f ...

EPFL2025

Nonlinear two-scale beam simulations accelerated by thermodynamics-informed neural networks

Konstantinos Karapiperis

We introduce an efficient computational framework for the simulation of complex beam networks and architected materials. At its core stands a thermodynamics-informed neural network, which serves as a surrogate material model for the cross-sectional respons ...

2024

Deterministic equivalent and error universality of deep random features learning

Hugo Chao Cui

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied ran ...

2024

Multi-Agent Learning in Contextual Games under Unknown Constraints

Maryam Kamgarpour, Anna Maria Maddux

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

2024

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Devis Tuia, Lloyd Haydn Hughes, Javiera Francisca Castillo Navarro, Valérie Zermatten

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

The Institute of Electrical and Electronics Engineers, Inc2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Alexis Berne, Anne-Claire Marie Billault--Roux, Gionata Ghiggi

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

COPERNICUS GESELLSCHAFT MBH2023

Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans

Antoine Jérôme Spahr

Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robus ...

2023

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Yasir Mahmood Qureshi, Joshua Alexander Harrison Klein

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

2022

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Barbara Bruno, Jauwairia Nasir

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

2022

High Resolution Interferometric Imaging of Liquid-Solid Interfaces with HOTNNET

John Martin Kolinski, Ramin Kaviani

Background A variety of imaging methods are available to obtain kinematic data at an interface, with a widely varying range of spatial and temporal resolution. These methods require a trade-off between imaging rate and resolution. Objective A deep learning ...

2022

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Damir Filipovic

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

WILEY2022

A fibre optic and machine learning based health monitoring system for superconducting power applications in modern supergrids

Arooj Akbar

The concerns for climate change and the transition to renewables is increasing the relevance of superconducting applications: High Temperature Superconductor (HTS) cables for efficient power transmission, superconducting magnets for nuclear fusion, superco ...

EPFL2022

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022

Semi-supervised Active Salient Object Detection

Gonzalo Orejas Guinea, Tong Zhang

In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset of the most discriminative and representative samples for labeling. Two main contributions have been made to prevent the me ...

ELSEVIER SCI LTD2022

Strong lensing in UNIONS: Toward a pipeline from discovery to modeling

Frédéric Courbin, Hung-Hsu Chan, Cameron Alexander Campbell Lemon, Georgios Vernardos, Benjamin Yvan Alexandre Clement, Karina Alexandra Rojas Olate, Elodie Marie Charlène Savary

We present a search for galaxy-scale strong gravitational lenses in the initial 2500 square degrees of the Canada-France Imaging Survey (CFIS). We designed a convolutional neural network (CNN) committee that we applied to a selection of 2 344 002 r-band im ...

EDP SCIENCES S A2022

Machine Learning for Musculoskeletal Modeling of Upper Extremity

Rahul Sharma

We propose a novel machine learning (ML)-driven methodology to estimate biomechanical variables of interest traditionally obtained from upper-extremity musculoskeletal (MSK) modeling. MSK models facilitate personalized modeling, perform "what-if" analyses, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Self-Supervision By Prediction For Object Discovery In Videos

Pascal Frossard, Beril Besbinar

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric mo ...

IEEE2021