Publications related to From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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

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 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

Linear Complexity Self-Attention With 3rd Order Polynomials

Grigorios Chrysos, Filippos Kokkinos

Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with the nu ...

Los Alamitos2023

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Christian Leinenbach, Sergey Shevchik, Rafal Wróbel

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

Lausanne2023

Visual question answering from another perspective: CLEVR mental rotation tests *

Sina Honari

Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made ...

ELSEVIER SCI LTD2023

Explicit representations for Banach subspaces of Lizorkin distributions

Michaël Unser, Sebastian Jonas Neumayer

The Lizorkin space is well suited to the study of operators like fractional Laplacians and the Radon transform. In this paper, we show that the space is unfortunately not complemented in the Schwartz space. In return, we show that it is dense in C0(Double- ...

WORLD SCIENTIFIC PUBL CO PTE LTD2023

Decentralized learning made easy with DecentralizePy

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Rishi Sharma, Milos Vujasinovic

Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. It consists of many nodes that coordinate without a central server and exchange millions of parameters in the inherently ite ...

2023

Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller

Hervé Lissek, Maxime Volery, Xinxin Guo

This paper presents an acoustic impedance control architecture for an electroacoustic absorber combining both feedforward and feedback microphone-based strategies on a current-driven loudspeaker. Feedforward systems enable good performance for direct imped ...

EDP SCIENCES S A2023

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

Universal Approximation Property of Hamiltonian Deep Neural Networks

Giancarlo Ferrari Trecate, Muhammad Zakwan

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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

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

Linear Complexity Self-Attention With 3rd Order Polynomials

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Visual question answering from another perspective: CLEVR mental rotation tests *

Explicit representations for Banach subspaces of Lizorkin distributions

Decentralized learning made easy with DecentralizePy

Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller

A Comparison of Residual-based Methods on Fault Detection

Universal Approximation Property of Hamiltonian Deep Neural Networks

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Explicit representations for Banach subspaces of Lizorkin distributions

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

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

Linear Complexity Self-Attention With 3rd Order Polynomials

Visual question answering from another perspective: CLEVR mental rotation tests *

Decentralized learning made easy with DecentralizePy

Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller

A Comparison of Residual-based Methods on Fault Detection

Universal Approximation Property of Hamiltonian Deep Neural Networks