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Deeplearning4j

Related publications (26)

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

Jan Sickmann Hesthaven, Federico Pichi

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 ...

Academic Press Inc Elsevier Science2024

STREAMING TENSOR TRAIN APPROXIMATION

Daniel Kressner

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

Siam Publications2023

Text Representation Learning for Low Cost Natural Language Understanding

Jan Frederik Jonas Florian Mai

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

EPFL2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

Olga Fink

Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic d ...

2023

Continuous-variable neural network quantum states and the quantum rotor model

Giuseppe Carleo

We initiate the study of neural network quantum state algorithms for analyzing continuous-variable quantum systems in which the quantum degrees of freedom correspond to coordinates on a smooth manifold. A simple family of continuous-variable trial wavefunc ...

SPRINGERNATURE2023

Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions

Olga Fink

Research Publishing2023

Hamiltonian reconstruction as metric for variational studies

Giuseppe Carleo

Variational approaches are among the most powerful techniques to approximately solve quantum many-body problems. These encompass both variational states based on tensor or neural networks, and parameterized quantum circuits in variational quantum eigensolv ...

2022

On combining denoising with learning-based image decoding

Touradj Ebrahimi, Michela Testolina

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captu ...

SPIE2022

Autoencoders reloaded

Hervé Bourlard, Selen Hande Kabil

In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposit ...

SPRINGER2022

Pre-bcc: A novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Hisham Tarek Mohamed Hafez

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environment ...

ELSEVIER SCI LTD2022

Relative stability toward diffeomorphisms indicates performance in deep nets

Matthieu Wyart, Mario Geiger, Leonardo Petrini, Alessandro Favero

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this qu ...

2021

Automatic Content Curation of Visual Heritage

Mathieu Salzmann, Frédéric Kaplan, Nicolas Henchoz, Delphine Ribes Lemay, Lara Jacqueline Défayes, André Viegas Andrade

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

2021

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

Alfio Quarteroni, Andrea Manzoni

The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models (FOMs). Likewise, the use of traditional reduced order models (ROMs) ...

FRONTIERS MEDIA SA2021

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Image Restoration using Plug-and-Play CNN MAP Denoisers

Sabine Süsstrunk, Siavash Arjomand Bigdeli, David Honzátko

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

SCITEPRESS2020

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Florent Gérard Krzakala, Jonathan Yuelin Dong

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...

Curran Associates, Inc.2020

Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition

Touradj Ebrahimi, Pinar Akyazi

In this paper, a learning-based image compression method that employs wavelet decomposition as a preprocessing step is presented. The proposed convolutional autoencoder is trained end-to-end to yield a target bitrate smaller than 0.15 bits per pixel across ...

2019

Full-Gradient Representation for Neural Network Visualization

François Fleuret, Suraj Srinivas

We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key p ...

2019

Residual Parameter Transfer for Deep Domain Adaptation

Pascal Fua, Mathieu Salzmann, Artem Rozantsev

The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representation ...

2018