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

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Olga Fink, Gaëtan Michel Frusque, Qi Li, Baorui Dai

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Research Publishing2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Euclid: Identification of asteroid streaks in simulated images using deep learning

Frédéric Courbin, Georges Meylan, Yi Wang, Richard Massey

The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition o ...

Les Ulis Cedex A2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque, Tianfu Li, Qi Li, Baorui Dai

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Devis Tuia, Marc Conrad Russwurm, Lloyd Haydn Hughes, Oleg Antropov, Sudipan Saha

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

The Institute of Electrical and Electronics Engineers, Inc2023

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Aude Billard, David Julian Gonon

We apply inverse reinforcement learning (IRL) with a novel cost feature to the problem of robot navigation in human crowds. Consistent with prior empirical work on pedestrian behavior, the feature anticipates collisions between agents. We efficiently learn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

Pascal Frossard, Seungryong Kim

Stereo confidence estimation aims to estimate the reliability of the estimated disparity by stereo matching. Different from the previous methods that exploit the limited input modality, we present a novel method that estimates confidence map of an initial ...

IEEE COMPUTER SOC2023

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

Mohammad Khaja Nazeeruddin, Paul Joseph Dyson, Yong Ding, Li Tao, Yao Zhang, Jun Zhang, Bowen Jiang

Hybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable ...

WILEY2023

A Comparison of Residual-based Methods on Fault Detection

Olga Fink, Gaëtan Michel Frusque

An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over tim ...

PHM Society2023

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

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Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Euclid: Identification of asteroid streaks in simulated images using deep learning

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

A Comparison of Residual-based Methods on Fault Detection

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Euclid: Identification of asteroid streaks in simulated images using deep learning

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

A Comparison of Residual-based Methods on Fault Detection

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks