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Publications associées (33)

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer, Stanislas Ducotterd

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

EPFL Infoscience2023

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

Mathieu Salzmann, Yinlin Hu, Fulin Liu

Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the i ...

Ieee Computer Soc2023

The irreducibility of vision: Gestalt, crowding and the fundamentals of vision

Michael Herzog

What is fundamental in vision has been discussed for millennia. For philosophical realists and the physiological approach to vision, the objects of the outer world are truly given, and failures to perceive objects properly, such as in illusions, are just s ...

2022

First-person experience cannot rescue causal structure theories from the unfolding argument

Michael Herzog, Adrien Christophe Doerig, Aaron Schurger

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the cri ...

Elsevier2022

Towards a pragmatist dealing with algorithmic bias in medical machine learning

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One ...

2021

Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images

Pascal Fua, Mateusz Kozinski, Leonardo Citraro

Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application. We show that these inconsistencies stem from design flaws that make the metri ...

2020

Extending Boolean Methods for Scalable Logic Synthesis

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù, Eleonora Testa

In recent years, Boolean methods in logic synthesis have been drawing the attention of EDA researchers due to the continuous push to advance quality of results. Boolean methods require high computational cost, as they rely on complete functional properties ...

2020

Decoding Cognitive States under Varying Difficulty Levels

Ping-Keng Jao

Understanding cognitive states of human under different difficulty levels is useful in improving human-human and human-machine interactions. For example, a crucial factor in designing games is maintaining the engagement of players. An ideal scenario would ...

EPFL2020

Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Datase

Davide Scaramuzza, Titus Cieslewski

Despite impressive results in visual-inertial state estimation in recent years, high speed trajectories with six degree of freedom motion remain challenging for existing estimation algorithms. Aggressive trajectories feature large accelerations and rapid r ...

2019

Fighting Rumours on Social Media

Chi Thang Duong

With the advance of social platforms, people are sharing contents in an unprecedented scale. This makes social platforms an ideal place for spreading rumors. As rumors may have negative impacts on the real world, many rumor detection techniques have been p ...

2018

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