Publications related to Inclusion of an automatic adaptive sampling method in NEC

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Directional Local Field Potentials in the Subthalamic Nucleus During Deep Brain Implantation of Parkinson's Disease Patients

André Mercanzini, Thuy Anh Khoa Nguyen

Segmented deep brain stimulation leads feature directional electrodes that allow for a finer spatial control of electrical stimulation compared to traditional ring-shaped electrodes. These segmented leads have demonstrated enlarged therapeutic windows and ...

2020

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Michaël Unser, Quy Ha Nguyen

In our recent work, the sampling and reconstruction of non-decaying signals, modeled as members of weighted-L-p spaces, were shown to be stable with an appropriate choice of the generating kernel for the shift-invariant reconstruction space. In this paper, ...

2019

Fast Mean Estimation with Sub-Gaussian Rates

Nicolas Henri Bernard Flammarion

We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion. Our analysis does not require global contractivity, and yields polynomial dependence on the time horizon. Compared to existing approaches, we make an additional ...

2019

Retroactive Packet Sampling for Traffic Receipts

Pavlos Nikolopoulos, Adrian Perrig, Christos Pappas

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this syst ...

ACM2019

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

How does the streamwater quality change during storm events, and how to characterize it? The purpose of this study is to investigate the evolution of major ions in streamwater from base ow to storm ux. Regular manual sampling and automated sampling campaig ...

2017

Inclusion of an automatic adaptive sampling method in NEC

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Collaborative Sampling in Generative Adversarial Networks

Directional Local Field Potentials in the Subthalamic Nucleus During Deep Brain Implantation of Parkinson's Disease Patients

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Fast Mean Estimation with Sub-Gaussian Rates

Retroactive Packet Sampling for Traffic Receipts

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Fast Mean Estimation with Sub-Gaussian Rates

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

Collaborative Sampling in Generative Adversarial Networks

Directional Local Field Potentials in the Subthalamic Nucleus During Deep Brain Implantation of Parkinson's Disease Patients

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Retroactive Packet Sampling for Traffic Receipts