Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

In this thesis, we give new protocols that offer a quantum advantage for problems in ML, Physics, and Finance. Quantum mechanics gives predictions that are inconsistent with local realism. The experiment proving this fact (Bell, 1964) gives a quantum proto ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

Lensless inline holography can produce high-resolution images over a large field of view (FoV). In a previous work [Appl. Opt.60, B38 (2021)], we showed that (i) the actual FoV can be extrapolated outside of the camera FoV and (ii) the effective resolution ...

Sparse recovery is a powerful tool that plays a central role in many applications, including source estimation in radio astronomy, direction of arrival estimation in acoustics or radar, super-resolution microscopy, and X-ray crystallography. Conventional a ...

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Chattez avec Graph Search

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

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

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Towards Trustworthy Deep Learning for Image Reconstruction

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Quantum Protocols for ML, Physics, and Finance

Efficient local linearity regularization to overcome catastrophic overfitting

A constrained method for lensless coherent imaging of thin samples

Looking beyond Pixels : Theory, Algorithms and Applications of Continuous Sparse Recovery