Publications related to Perceptrons (book)

A new variable shape parameter strategy for RBF approximation using neural networks

The choice of the shape parameter highly effects the behaviour of radial basis function (RBF) approximations, as it needs to be selected to balance between the ill-conditioning of the interpolation matrix and high accuracy. In this paper, we demonstrate ho ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Privacy-preserving federated neural network training and inference

Sinem Sav

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

EPFL2023

Deep Learning Detection of GPS Spoofing

Mirjana Stojilovic, Olivia Jullian Parra

Unmanned aerial vehicles (UAVs) are widely deployed in air navigation, where numerous applications use them for safety-of-life and positioning, navigation, and timing tasks. Consequently, GPS spoofing attacks are more and more frequent. The aim of this wor ...

Springer, Cham2022

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN), to perform classification tasks. There have been many proposals on how to use variational quantum circuits as ...

2022

Shrinking Bouma's window: How to model crowding in dense displays

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements ...

2021

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Farzaneh Talebkeikhah, Mohsen Talebkeikhah

Transformer oil-based nanofluids are known to have higher thermal conductivity and heat transfer performance compared to conventional transformer oils. In this study, four different types of transformer oil-based nanofluids are synthesized using the well-k ...

ELSEVIER2020

Investigation of effective processes parameters on lead (II) adsorption from wastewater by biochar in mild air oxidation pyrolysis process

Farzaneh Talebkeikhah, Mohsen Talebkeikhah, Mohammad Torkashvand

This study evaluates and compares several machine learning methods on the effects of different parameters in lead adsorption capacity. pH, contact time, adsorbent dosage and initial lead concentration were considered as inputs and adsorption capacity was r ...

TAYLOR & FRANCIS LTD2022

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

Scaling description of generalization with number of parameters in deep learning

Matthieu Wyart, Clément Hongler, Mario Geiger, Stefano Spigler, Levent Dogus Sagun, Franck Raymond Gabriel, Arthur Ulysse Jacot-Guillarmod, Arthur Jacot

Supervised deep learning involves the training of neural networks with a large number N of parameters. For large enough N, in the so-called over-parametrized regime, one can essentially fit the training data points. Sparsitybased arguments would suggest th ...

IOP PUBLISHING LTD2020

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Farzaneh Talebkeikhah, Mohsen Talebkeikhah

This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amou ...

TAYLOR & FRANCIS LTD2021

Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

Marko Baros, Ivan Dimitrijevic

Colorectal carcinoma (CRC) as a major health problem in industrialized countries is highly preventable and can be successfully treated in the early stages. However, incidence and mortality of CRC has increased over the last two decades. The reason could be ...

2019

Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing

Christian Enz, Vincent Frédéric Camus

The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...

IEEE2019

Machine-learning Classifiers for Intermediate Redshift Emission-line Galaxies

Jean-Paul Richard Kneib, Johan Comparat, Christoph Ernst René Schäfer, David Schlegel

Classification of intermediate redshift (z = 0.3-0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGNs), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was impossible because ...

IOP PUBLISHING LTD2019

Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

Jean-Marc Odobez, Olivier Canévet

We propose a head pose estimation framework that leverages on a recent keypoint detection model. More specifically, we apply the convolutional pose machines (CPMs) to input images, extract different types of facial keypoint features capturing appearance in ...

IEEE2018

An artificial neural network as a troubled-cell indicator

Jan Sickmann Hesthaven, Deep Ray

High-resolution schemes for conservation laws need to suitably limit the numerical solution near discontinuities, in order to avoid Gibbs oscillations. The solution quality and the computational cost of such schemes strongly depend on their ability to corr ...

2018

On Learning to Identify Genders from Raw Speech Signal Using CNNs

Hannah Muckenhirn, Selen Hande Kabil

Automatic Gender Recognition (AGR) is the task of identifying the gender of a speaker given a speech signal. Standard approaches extract features like fundamental frequency and cepstral features from the speech signal and train a binary classifier. Inspire ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Mixed-precision architecture based on computational memory for training deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

IEEE2018

Learning Network Structures from Firing Patterns

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes’ behaviors (e.g., the time ...

Ieee2016

Towards End-to-End Speech Recognition

Dimitri Palaz

Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...

EPFL2016

Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

Irem Boybat Kara, Carmelo Di Nolfo

A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, an ...

IEEE2015