Robust Training and Verification of Deep Neural Networks

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The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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 ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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 ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

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 ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Robust Training and Verification of Deep Neural Networks

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Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Partial discharge localization in power transformer tanks using machine learning methods

Residual-based attention in physics-informed neural networks

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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

Incorporating Projective Geometry into Deep Learning

Sparse autoregressive neural networks for classical spin systems

Aggregating Spatial and Photometric Context for Photometric Stereo

On the number of regions of piecewise linear neural networks

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning