Publications related to MLP Based Hierarchical System for Task Adaptation in ASR

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

An exact mapping from ReLU networks to spiking neural networks

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

2023

Learning multivariate new physics

Raffaele Tito D'Agnolo, Andrea Wulzer

We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is designed to be sensi ...

SPRINGER2021

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

Disentangling feature and lazy training in deep neural networks

Matthieu Wyart, Mario Geiger, Stefano Spigler, Arthur Jacot

Two distinct limits for deep learning have been derived as the network width h -> infinity, depending on how the weights of the last layer scale with h. In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described b ...

IOP PUBLISHING LTD2020

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

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

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

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Charlotte Marie Bürki

Air pollutants emissions in urban areas are numerous and heterogeneous. Traditional monitoring techniques are restricted to a few highquality instruments missing this spatial heterogeneity. Recently, great interest has been given to low-cost sensors to bet ...

2017

MLP Based Hierarchical System for Task Adaptation in ASR

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Benign Overfitting in Deep Neural Networks under Lazy Training

An exact mapping from ReLU networks to spiking neural networks

Learning multivariate new physics

Mixed-Precision Deep Learning Based on Computational Memory

Disentangling feature and lazy training in deep neural networks

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

On Learning to Identify Genders from Raw Speech Signal Using CNNs

Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Mixed-Precision Deep Learning Based on Computational Memory

Benign Overfitting in Deep Neural Networks under Lazy Training

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

Learning multivariate new physics

An exact mapping from ReLU networks to spiking neural networks

Disentangling feature and lazy training in deep neural networks

Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

On Learning to Identify Genders from Raw Speech Signal Using CNNs