Publications related to Comparison of Support Vector Machine and Neural Network for Text Texture Verification

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

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-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

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

Jan Sickmann Hesthaven

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

Sharp asymptotics on the compression of two-layer neural networks

Marco Mondelli

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...

IEEE2022

Sound and Complete Verification of Polynomial Networks

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Elias Abad Rocamora, Mehmet Fatih Sahin

Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Exist ...

2022

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

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In this paper, we study an emerging class of neural networks, the Morphological Neural networks, from some modern perspectives. Our approach utilizes ideas from tropical geometry and mathematical morphology. First, we state the training of a binary morphol ...

IEEE2021

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

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The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

James Henderson, Alireza Mohammadshahi

We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

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

Mohsen Talebkeikhah, Farzaneh 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

Comparison of Support Vector Machine and Neural Network for Text Texture Verification

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Benign Overfitting in Deep Neural Networks under Lazy Training

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

Privacy-preserving federated neural network training and inference

Sharp asymptotics on the compression of two-layer neural networks

Sound and Complete Verification of Polynomial Networks

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Perceiving Humans: from Monocular 3D Localization to Social Distancing

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Sound and Complete Verification of Polynomial Networks

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Perceiving Humans: from Monocular 3D Localization to Social Distancing

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

Benign Overfitting in Deep Neural Networks under Lazy Training

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

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

Sharp asymptotics on the compression of two-layer neural networks

Privacy-preserving federated neural network training and inference