Publications associées à Covariance

Idiap submission to the NIST SRE 2019 Speaker Recognition Evaluation

Idiap has made a submission to the conversational telephony speech (CTS) challenge of the NIST SRE 2019. The submission consists of six speaker verification (SV) systems: four extended TDNN (E-TDNN) and two TDNN x-vector systems. Employment of various trai ...

Idiap2019

MATHICSE Technical Report : Efficient state/Parameter estimation in nonlinear unsteady PDEs by reduced basis ensemble Kalman filter

Alfio Quarteroni, Andrea Manzoni, Stefano Pagani

The ensemble Kalman filter is a computationally efficient technique to solve state and/or parameter estimation problems in the framework of statistical inversion when relying on a Bayesian paradigm. Unfortunately its cost may become moderately large for sy ...

MATHICSE2019

A Machine-Learning Framework for Automating Well-Log Depth Matching

Timon Zimmermann

Depth matching well logs acquired from multiple logging passes in a single well has been a longstanding challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance m ...

SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA2019

Reconstructing the Drizzle Mode of the Raindrop Size Distribution Using Double-Moment Normalization

Alexis Berne, Timothy Hugh Raupach

Commonly used disdrometers tend not to accurately measure concentrations of very small drops in the raindrop size distribution (DSD), either through truncation of the DSD at the small-drop end or because of large uncertainties on these measurements. Recent ...

AMER METEOROLOGICAL SOC2019

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Mohammad Emtiyaz Khan

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Chebyshev Inequalities for Products of Random Variables

Daniel Kuhn, Napat Rujeerapaiboon, Wolfram Wiesemann

We derive sharp probability bounds on the tails of a product of symmetric non-negative random variables using only information about their first two moments. If the covariance matrix of the random variables is known exactly, these bounds can be computed nu ...

2018

New Approach to Identifying Critical Initial Conditions for Extreme Flood Simulations in a Semicontinuous Simulation Framework

Anton Schleiss, Javier García Hernández, Bettina Schaefli, Fränz Zeimetz, Guillaume Mathieu Artigue

Extreme flood simulation with synthetic extreme precipitation events raises unavoidable questions about the choice of initial conditions. State-of-the-art extreme flood estimation frameworks propose to address these questions with the help of semicontinuou ...

2018

A Novel Method for Sampling Bandlimited Graph Signals

Pascal Frossard, Pinar Akyazi, Dion Eustathios Olivier Tzamarias

In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...

IEEE COMPUTER SOC2018

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Covariance

Idiap submission to the NIST SRE 2019 Speaker Recognition Evaluation

MATHICSE Technical Report : Efficient state/Parameter estimation in nonlinear unsteady PDEs by reduced basis ensemble Kalman filter

Revealing evolutionary constraints on proteins through sequence analysis

A Machine-Learning Framework for Automating Well-Log Depth Matching

Reconstructing the Drizzle Mode of the Raindrop Size Distribution Using Double-Moment Normalization

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Chebyshev Inequalities for Products of Random Variables

New Approach to Identifying Critical Initial Conditions for Extreme Flood Simulations in a Semicontinuous Simulation Framework

On the Comparison of Gauge Freedom Handling in Optimization-Based Visual-Inertial State Estimation

A Novel Method for Sampling Bandlimited Graph Signals

Graph Chatbot

Chattez avec Graph Search

MATHICSE Technical Report : Efficient state/Parameter estimation in nonlinear unsteady PDEs by reduced basis ensemble Kalman filter

A Machine-Learning Framework for Automating Well-Log Depth Matching

Revealing evolutionary constraints on proteins through sequence analysis

Idiap submission to the NIST SRE 2019 Speaker Recognition Evaluation

A Novel Method for Sampling Bandlimited Graph Signals

Reconstructing the Drizzle Mode of the Raindrop Size Distribution Using Double-Moment Normalization

Chebyshev Inequalities for Products of Random Variables

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

New Approach to Identifying Critical Initial Conditions for Extreme Flood Simulations in a Semicontinuous Simulation Framework

On the Comparison of Gauge Freedom Handling in Optimization-Based Visual-Inertial State Estimation