From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging

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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 ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

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 ...

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 ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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 ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed thr ...

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging

Chat with Graph Search

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Optimizing Dynamic Aperture Studies with Active Learning

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

Gibbs sampling the posterior of neural networks

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

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

On the number of regions of piecewise linear neural networks

Enabling Uncertainty Estimation in Iterative Neural Networks

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

Graph generative deep learning models with an application to circuit topologies

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Estimation of horizontal running power using foot-worn inertial measurement units