Publications related to Deep Learning Generalization with Limited and Noisy Labels

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

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

IOP Publishing Ltd2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith

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

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Dimitrios Kaminaris

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

Basel2023

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the d ...

2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Alessandro Mapelli, Radoslav Marchevski, Alina Kleimenova

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decis ...

New York2023

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

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

Olga Fink, Jian Zhou

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

Deep Learning Generalization with Limited and Noisy Labels

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

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Towards learning-based denoising of light fields

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Improved calorimetric particle identification in NA62 using machine learning techniques

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

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

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Towards learning-based denoising of light fields

Improved calorimetric particle identification in NA62 using machine learning techniques

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

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

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

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

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