Publications associées à Time delay neural network

Publications associées (29)

Linear Complexity Self-Attention With 3rd Order Polynomials

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 ...

Ieee Computer Soc2023

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

Sabine Süsstrunk, Mathieu Salzmann, Chen Liu, Yulun Jiang

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

Transformer Models for Vision

Jean-Baptiste Francis Marie Juliette Cordonnier

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

EPFL2023

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Willem Anna Mark Wybo, Bernd Albert Illing

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contex-tual sta ...

National Academy of Sciences2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

De novo protein design by inversion of the AlphaFold structure prediction network

Bruno Emanuel Ferreira De Sousa Correia, Stéphane Rosset, Casper Alexander Goverde, Hamed Khakzad, Benedict Dieter Gregor Wolf

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in comp ...

2023

Sharp asymptotics on the compression of two-layer neural networks

Marco Mondelli, Mohammad Hossein Amani

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

Improving the Training of Compact Neural Networks for Visual Recognition

Shuxuan Guo

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

EPFL2022

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

Qian Wang, Tobias Kober, Tom Hilbert

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

SPRINGER2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Structured cerebellar connectivity supports resilient pattern separation

Ilaria Ricchi

The cerebellum is thought to help detect and correct errors between intended and executed commands(1,2) and is critical for social behaviours, cognition and emotion 3 Computations for motor control must be performed quicklyto correct errors in real time an ...

NATURE PORTFOLIO2023

A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs

Silvestro Micera, Ivo Strauss, Leonardo Pollina

Objective. Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation o ...

IOP Publishing Ltd2022

Impact and implications of mixed plaque class in automated characterization of complex atherosclerotic lesions

Yanan Niu

Atherosclerosis is a complex disease altering vasculature morphology, and subsequently flow, with progressive plaque formation, mural disruption, and lumen occlusion. Determination of clinically-relevant plaque components-particularly calcium, lipid, and f ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Multivariate time series forecasting for freeway networks

Semin Kwak

This dissertation introduces traffic forecasting methods for different network configurations and data availability. Chapter 2 focuses on single freeway cases. Although its topology is simple, the non-linearity of traffic features makes this prediction sti ...

EPFL2022

Approximating Relu Networks By Single-Spike Computation

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

Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

IEEE2022

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2022

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

Biologically plausible unsupervised learning in shallow and deep neural networks

Bernd Albert Illing

The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

EPFL2021

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2022