Publications related to All models are wrong

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Bahar Aydemir, Ludo Hoffstetter

Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider t ...

2023

Multi-task prompt-RSVQA to explicitly count objects on aerial images

Devis Tuia, Sylvain Lobry, Christel Marie Tartini-Chappuis, Javiera Francisca Castillo Navarro, Nicola Antonio Santacroce

Introduced to enable a wider use of Earth Observation images using natural language, Remote Sensing Visual Question Answering (RSVQA) remains a challenging task, in particular for questions related to counting. To address this specific challenge, we propos ...

2023

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Zhen Wei, Zhiye Wang, Peixia Li

Person retrieval aims at effectively matching the pedestrian images over an extensive database given a specified identity. As extracting effective features is crucial in a high-performance retrieval system, recent significant progress was achieved by part- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

SAGE PUBLICATIONS LTD2021

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Product of experts for robot learning from demonstration

Emmanuel Pignat

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

EPFL2021

Dealing with Correlations in Discrete Choice Models

Anna Fernández Antolín

The focus of this thesis is to develop methods to address research challenges related to correlation patterns in discrete choice models. In the context of correlations within alternatives, we extend the novel methodology of the multiple indicator solution ...

EPFL2018

Inference of genealogies with geolocalised genetic data

Natural populations present an abundant genetic variability. Like mutation or natural se- lection, dierent processes are at stake to generate this variability. Population genetics is a topic that emerged in the late 40's, thanks mainly to the biologists Fi ...

2017

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Aurélien Gallice, Sebastiano Piccolroaz, Marco Toffolon, Elisa Calamita

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data-de ...

Wiley-Blackwell2016

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

All models are wrong

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TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Multi-task prompt-RSVQA to explicitly count objects on aerial images

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Discrete-Choice Mining of Social Processes

Product of experts for robot learning from demonstration

Dealing with Correlations in Discrete Choice Models

Inference of genealogies with geolocalised genetic data

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Discrete-Choice Mining of Social Processes

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Multi-task prompt-RSVQA to explicitly count objects on aerial images

Product of experts for robot learning from demonstration

Dealing with Correlations in Discrete Choice Models

Inference of genealogies with geolocalised genetic data

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Sparsistency of $\ell_1$-Regularized $M$-Estimators