Publications related to On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Network time series forecasting in photovoltaics power production

Jelena Simeunovic

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

EPFL2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Alireza Modirshanechi

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Robust NAS under adversarial training: benchmark, theory, and beyond

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

2024

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Devis Tuia, Gaston Jean Lenczner, Jan Dirk Wegner, Jan Pisl, Marc Conrad Russwurm, Lloyd Haydn Hughes

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

Iop Publishing Ltd2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Amsterdam2024

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Loris Di Natale

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

EPFL2024

On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI

Graph Chatbot

Chat with Graph Search

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Network time series forecasting in photovoltaics power production

Understanding generalization and robustness in modern deep learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Understanding generalization and robustness in modern deep learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Robust NAS under adversarial training: benchmark, theory, and beyond

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Network time series forecasting in photovoltaics power production

Mapping drivers of tropical forest loss with satellite image time series and machine learning

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification