High Resolution Interferometric Imaging of Liquid-Solid Interfaces with HOTNNET

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.

Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied ran ...

The diagnosis of sleep disorders is still often based on polysomnography, an in-lab exam allowing experts to perform accurate sleep staging, although this is labor-intensive, expensive, and exposing patients to unusual sleep conditions. A state-of-the-art ...

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks. Yet, the extent to which DNNs capture fundamental aspects of human vision such ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robus ...

BackgroundHigh-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for a ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regulariz ...

The time-resolved visualization of the dynamics of a cavitation bubble usually requires the use of expensive high-speed cameras, which often provide a limited spatial resolution. In the present study, we propose an alternative to these high-speed imaging t ...

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

The concerns for climate change and the transition to renewables is increasing the relevance of superconducting applications: High Temperature Superconductor (HTS) cables for efficient power transmission, superconducting magnets for nuclear fusion, superco ...

High Resolution Interferometric Imaging of Liquid-Solid Interfaces with HOTNNET

Chattez avec Graph Search

Dependable learning and control through Neural ODEs

Controlled learning of pointwise nonlinearities in neural-network-like architectures

Deterministic equivalent and error universality of deep random features learning

PPG-Based Sleep Staging Using SleepPPGNet: Extension to Wearables, Improvements, Limitations

Multi-Agent Learning in Contextual Games under Unknown Constraints

Divergences in color perception between deep neural networks and humans

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans

CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Stability of Image-Reconstruction Algorithms

Ex uno plures: how to construct high-speed movies of collapsing cavitation bubbles from a single image

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

A fibre optic and machine learning based health monitoring system for superconducting power applications in modern supergrids