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Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Anders Meibom, Devis Tuia, Guilhem Maurice Louis Banc-Prandi, Jonathan Paul Sauder

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with De ...

EPFL Infoscience2024

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

New York2023

Multi-agent Reinforcement Learning for Assembly of a Spanning Structure

Gabriel Rémi Vallat

In this master thesis, multi-agent reinforcement learning is used to teach robots to build a self-supporting structure connecting two points. To accomplish this task, a physics simulator is first designed using linear programming. Then, the task of buildin ...

2023

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Daniel Probst

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 ...

2023

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

New York2023

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Olga Fink, Mina Montazeri

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

PERGAMON-ELSEVIER SCIENCE LTD2023

In the context of automatic visual inspection of infrastructures by drones, Deep Learning (DL) models are used to automatically process images for fault diagnostics. While explainable Artificial Intelligence (AI) algorithms can provide explanations to asse ...

Research Publishing2023

Automatic table detection and classification in large-scale newspaper archives

In recent decades, major efforts to digitize historical documents led to the creation of large machine readable corpora, including newspapers, which are waiting to be processed and analyzed. Newspapers are a valuable historical source, notably because of t ...

2022

The aging brain as a model to understand motor skill acquisition in humans and its restoration using non-invasive brain stimulation

Pablo Maceira Elvira

Motor skill acquisition is essential to our survival, as it enables an efficient interaction with the changing world around us. The acquisition of novel sequential tasks, of particular relevance due to their pervasiveness in everyday life activities, can b ...

EPFL2022