Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

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Publications associées (31)

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Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an extended overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and ...

CEUR-WS2020

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Abu Sebastian

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

IEEE2019

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Alexandre Massoud Alahi, Sven Kreiss, Yuejiang Liu

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

IEEE2019

Reverse engineering the motor control system

Berat Denizdurduran

Mammalian motor control is implemented by a combination of different networks and system, working coherently to plan the movement of the body or a limb and to execute this movement a dynamical environment. While it is believed that complex, voluntary movem ...

EPFL2019

Machine Learning in High Energy Physics Community White Paper

João Miguel das Neves Duarte, Siyuan Wang, Conor Thomas Fitzpatrick

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction ...

IOP PUBLISHING LTD2018

Collecting Experience Data from Remotely Hosted Learning Applications

Denis Gillet, Christophe Salzmann

The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technol ...

SPRINGER2018

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Helge Jochen Rhodin, Isinsu Katircioglu, Victor Constantin

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Ma ...

IEEE2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018

Deep Convolutional Neural Network for Inverse Problems in Imaging

Michaël Unser, Michael Thompson McCann, Kyong Hwan Jin, Emmanuel Augustin Julien Froustey

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

2017

Word Sequence Modeling using Deep Learning : an End-to-end Approach and its Applications

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Learning Data Triage: Linear Decoding Works for Compressive MRI

Volkan Cevher, Yen-Huan Li

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for ...

Ieee2016