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OpenCV

Publications associées (17)

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Safe Deep Neural Networks

Kyle Michael Matoba

				The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...

EPFL2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Gaston Jean Lenczner, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Liang Xu

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

2023

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Ahmet Caner Yüzügüler

Deep neural networks (DNN) have become an essential tool to tackle challenging tasks in many fields of computer science. However, their high computational complexity limits their applicability. Specialized DNN accelerators have been developed to accommodat ...

EPFL2023

Theory of Deep Learning: Neural Tangent Kernel and Beyond

Arthur Ulysse Jacot-Guillarmod

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

EPFL2022

COBRA: Enhancing DNN Latency Prediction with Language Models trained on Source Code

Robin Adrien Zbinden

With the recent developments of Deep Learning, having an accurate and device specific latency prediction for Deep Neural Networks (DNNs) has become important for both the manual and automatic design of efficient DNNs. Directly predicting the latency of DNN ...

2022

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero, Amir Rezaie

We present a pipeline for the automatic generation of digital twins of free-standing buildings for damage assessment. Our method takes multiple view images of the target building as input and processes them to produce a 3D model in levels of detail (LOD) f ...

2022

Deep Neural Networks for seeing through multimode fibers

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava

Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a speckle pattern at the fiber output. In this work, we use Deep Neural Networks (DNNs) for recovery and/or classification of the input image from the intensity-o ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Analysis of Language Dependent Front-End for Speaker Recognition

Petr Motlicek, Subhadeep Dey

In Deep Neural Network (DNN) i-vector based speaker recognition systems, acoustic models trained for Automatic Speech Recognition are employed to estimate sufficient statistics for i-vector modeling. The DNN based acoustic model is typically trained on a w ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Phonetic aware techniques for Speaker Verification

Subhadeep Dey

The goal of this thesis is to improve current state-of-the-art techniques in speaker verification (SV), typically based on âidentity-vectorsâ (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using variou ...

EPFL2018