Time delay neural network

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Machine learning
Artificial neural networks

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Gradient-based Methods for Deep Model Interpretability

Suraj Srinivas

In this dissertation, we propose gradient-based methods for characterizing model behaviour for the purposes of knowledge transfer and post-hoc model interpretation. Broadly, gradients capture the variation of some output feature of the model upon unit vari ...

EPFL2021

Joint Human Pose Estimation and Stereo 3D Localization

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni, Wenlong Deng

We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composit ...

IEEE2020

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

ELSEVIER2020

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Michael Herzog, Aurélien Bornet, Adrien Christophe Doerig, Oh-Hyeon Choung

Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested t ...

2020

Neural Anisotropy Directions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depen ...

2020

An edge detector based on artificial neural network with application to hybrid Compact-WENO finite difference schemes

Jan Sickmann Hesthaven, Zhen Gao

A new approach is proposed to detect edges based on an artificial neural network (ANN). Some elementary continuous and discontinuous functions interpolated in the polynomial space and their continuity are used as the training sets to train a back propagati ...

Springer/Plenum2020

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Sai Praneeth Reddy Karimireddy, Sebastian Urban Stich

We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two terms: (i) a stocha ...

Microtome Publishing2020

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Mahsa Shoaran, Bingzhao Zhu

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Disentangling feature and lazy training in deep neural networks

Matthieu Wyart, Mario Geiger, Stefano Spigler, Arthur Ulysse Jacot-Guillarmod, 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

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