Jamming transition as a paradigm to understand the loss landscape of deep neural networks

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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 ...

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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Jamming transition as a paradigm to understand the loss landscape of deep neural networks

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Understanding generalization and robustness in modern deep learning

Robust machine learning for neuroscientific inference

Aggregating Spatial and Photometric Context for Photometric Stereo

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Incorporating Projective Geometry into Deep Learning

On the number of regions of piecewise linear neural networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Measurement - Based/Model-Less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Predicting the long-term collective behaviour of fish pairs with deep learning

Network time series forecasting in photovoltaics power production

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Partial discharge localization in power transformer tanks using machine learning methods