On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

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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 ...

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

2

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In total, 28 of the 29 fish species reported from the Lake Kivu basin occur in the littoral zone of the lake, but information about their structure, occurrence, and the habitats affecting their distribution is largely lacking. The lake's inshore area is po ...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

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Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

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Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

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On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

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Understanding generalization and robustness in modern deep learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

Social media and deep learning reveal specific cultural preferences for biodiversity

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Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Chronic warming and dry soils limit carbon uptake and growth despite a longer growing season in beech and oak

Fish Diversity in Relation to Littoral Habitats in Three Basins of Lake Kivu (East Africa)

Expectation consistency for calibration of neural networks

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Deep Learning Generalization with Limited and Noisy Labels

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Exploring neural networks and their potential for species distribution modeling

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End-to-end kernel learning via generative random Fourier features

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Polynomial-time universality and limitations of deep learning