Publications associées à Rprop

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...

2024

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Ali H. Sayed, Stefan Vlaski

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...

INT PRESS BOSTON, INC2020

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged ...

Curran Associates, Inc.2020

Understanding and Improving Fast Adversarial Training

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko

A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that ℓ∞-adversarial training with fast gradient sign method (FGSM) can fail due to a phenomenon called ...

2020

Backpropagation-Friendly Eigendecomposition

Pascal Fua, Mathieu Salzmann, Zheng Dang, Wei Wang, Yinlin Hu

Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it with the Power Iteration method, particularly when dealing with large matri ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Jan Sickmann Hesthaven, Stefano Ubbiali

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Alessandro Fumarola, Severin Sidler

Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductanc ...

Ieee2016

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Rprop

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Biologically plausible unsupervised learning in shallow and deep neural networks

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Understanding and Improving Fast Adversarial Training

Backpropagation-Friendly Eigendecomposition

Biologically plausible deep learning – but how far can we go with shallow networks?

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Graph Chatbot

Chattez avec Graph Search

Biologically plausible unsupervised learning in shallow and deep neural networks

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Understanding and Improving Fast Adversarial Training

Backpropagation-Friendly Eigendecomposition

Biologically plausible deep learning – but how far can we go with shallow networks?

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Non-intrusive reduced order modeling of nonlinear problems using neural networks