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Related publications (30)

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Michele Bianco, Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Artificial intelligence for natural product drug discovery

Philippe Schwaller, Albert Hofstetter, Michael Alexander Skinnider, Florian Huber

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Nature Portfolio2023

Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection

Olga Fink, Giovanni Floreale

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

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

Meritxell Bach Cuadra, Hamza Kebiri

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

Springer2023

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Florent Evariste Forest, Yunhong Che

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide t ...

Elsevier Sci Ltd2023

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Tanja Christina Käser Jacober, Vinitra Swamy, Mirko Marras, Sijia Du

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...

2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Tanja Christina Käser Jacober, Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution

Maria Brbic, Sandy Lee

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We c ...

American Association for the Advancement of Science2023

Text Representation Learning for Low Cost Natural Language Understanding

Jan Frederik Jonas Florian Mai

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

EPFL2023

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