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Publications associées (30)

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Nikolaos Geroliminis, Semin Kwak

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...

IEEE Institute of Electrical and Electronics Engineers2021

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Giuseppe Carleo, Julien Sebastian Gacon

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

2020

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

Scalable and Efficient Comparison-based Search without Features

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form “Which object from the pair (i, j) is more similar to t?”. Objects live in a space of latent features, from which the oracle generate ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

Cognitive workload monitoring in virtual reality based rescue missions with drones

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

2020

Kagome model for a Z(2) quantum spin liquid

Jonathan D'Emidio

We present a study of a simple model antiferromagnet consisting of a sum of nearest-neighbor SO(N) singlet projectors on the kagome lattice. Our model shares some features with the popular S = 1/2 kagome antiferromagnet but is specifically designed to be f ...

AMER PHYSICAL SOC2020

Triplet grain growth in a-texture polycrystalline ZnO thin films

Cécile Hébert, Duncan Thomas Lindsay Alexander, Arthur Brian Aebersold

A growth of grain triplets is identified in vapor deposited, a-texture polycrystalline zinc oxide thin films, using a combination of transmission electron microscopy-based automated crystal orientation mapping and high-resolution imaging. Each triplet cons ...

2020

Hardware Implementation of Neural Self-Interference Cancellation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yann Kurzo

In-band full-duplex systems can transmit and receive information simultaneously and on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference canc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Covering Convex Bodies and the Closest Vector Problem

Moritz Andreas Venzin, Márton Naszódi

We present algorithms for the (1+epsilon)-approximate version of the closest vector problem for certain norms. The currently fastest algorithm (Dadush and Kun 2016) for general norms in dimension n has running time of 2(O(n)) (1/epsilon)(n) . We improve th ...

2022