Publication
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Publications associées (32)
A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving
Richard Lee Davis, Engin Walter Bumbacher, Jérôme Guillaume Brender
How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...
Association for Computing Machinery2024Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry
While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...
2023Minimum Cost Intervention Design for Causal Effect Identification
Negar Kiyavash, Sina Akbari, Seyed Jalal Etesami
Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...
PMLR2022Democratizing Machine Learning
Nirupam Gupta, Alexandre David Olivier Maurer, Rafaël Benjamin Pinot
The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...
IEEE2022Discrete Optimal Transport with Independent Marginals is #P-Hard
Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Bahar Taskesen
We study the computational complexity of the optimal transport problem that evaluates the Wasser- stein distance between the distributions of two K-dimensional discrete random vectors. The best known algorithms for this problem run in polynomial time in th ...
2022Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning
Devis Tuia, Benjamin Alexander Kellenberger
The goal of habitat suitability mapping is to predict the lo-cations in which a given species could be present. This is typically accomplished by statistical models which use envi-ronmental variables to predict species observation data. The relationship be ...
IEEE2022A method for faster application of process integration techniques in retrofit situations
Numerous process integration techniques were proved to be highly effective for identifying and estimating potential energy savings in the industry. However, they require high time and effort to collect and analyse process data. As a result, they do not con ...
ELSEVIER SCI LTD2021Deep Learning Approaches for Auditory Perception in Robotics
Auditory perception is an essential part of a robotic system in Human-Robot Interaction (HRI), and creating an artificial auditory perception system that is on par with human has been a long-standing goal for researchers. In fact, this is a challenging res ...
EPFL2021Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas
Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical fo ...
2020