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Publications associées (31)

Gramatron: Effective Grammar-Aware Fuzzing

Mathias Josef Payer

Fuzzers aware of the input grammar can explore deeper program states using grammar-aware mutations. Existing grammar-aware fuzzers are ineffective at synthesizing complex bug triggers due to: (i) grammars introducing a sampling bias during input generation ...

ASSOC COMPUTING MACHINERY2021

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Alessandro Pau

This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrup ...

IOP Publishing Ltd2021

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

Michele Ceriotti, Gareth Aneurin Tribello, Federico Giberti

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

A systematic review of machine learning classification methodologies for modelling passenger mode choice

Michel Bierlaire, Timothy Michael Hillel, Ying Jin

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research ques ...

ELSEVIER SCI LTD2021

Functional registration and local variations: Identifiability, rank, and tuning

Victor Panaretos, Anirvan Chakraborty

We develop theory and methodology for the problem of nonparametric registration of functional data that have been subjected to random deformation (warping) of their time scale. The separation of this phase variation ("horizontal" variation) from the amplit ...

2021

Advances in Bias-aware Recommendation on the Web

Mirko Marras

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describi ...

ASSOC COMPUTING MACHINERY2021

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Mirko Marras

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing ...

IEEE COMPUTER SOC2021

Controlling the rotation of drift tearing modes by biased electrode in ADITYA-U tokamak

Harshita Raj, Ankit Kumar, Abhijit Sen, Rohit Kumar, Sameer Kumar

The influence of background plasma poloidal rotation on the rotation frequency of the m/n = 2/1 drift tearing mode (DTM) has been studied in ADITYA-U tokamak. The poloidal rotation velocity of the background plasma in the ion diamagnetic direction is incre ...

2021

Toward a remote assessment of walking bout and speed: application in patients with multiple sclerosis

Kamiar Aminian, Farzin Dadashi, Arash Atrsaei, Benoît Mariani

Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gai ...

2021

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