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On the Double Descent of Random Features Models Trained with SGD

Publications associées (36)

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Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of ...

EPFL2015

Linear Bayesian Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurately estimating the system dynamics, and in particular when we allow for corre ...

2013

Effective Enrichment of Gene Expression Data Sets

Utku Sirin

The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this pap ...

2012

Sampling and Reconstructing Diffusion Fields with Localized Sources

Martin Vetterli, Yue Lu, Juri Ranieri, Amina Chebira

We study the spatiotemporal sampling of a diffusion field generated by K point sources, aiming to fully reconstruct the unknown initial field distribution from the sample measurements. The sampling operator in our problem can be described by a matrix deriv ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Martin Vetterli, Olivier Roy, Yue Lu, Ali Hormati

We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compres ...

2010

A Concurrent Quadrature Sub-Sampling Mixer for Multiband Receivers

Christian Enz, Aravind Prasad Heragu Singaiyengar

A quadrature sub-sampling direct conversion mixer capable of sampling two or more bands concurrently using a single sampling frequency is presented. The implementation of the mixer to sample a band in quadrature and downconvert it to baseband is discussed ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Sampling of Alternatives for Route Choice Modeling

Michel Bierlaire, Emma Frejinger

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin-destination pair. Although this is behaviorally questionable, we make this ...

2009

Dynamic Partitioned Sampling For Tracking With Discriminative Features

Jean-Marc Odobez

We present a multi-cue fusion method for tracking with particle filters which relies on a novel hierarchical sampling strategy. Similarly to previous works, it tackles the problem of tracking in a relatively high-dimensional state space by dividing such a ...

2009

Wave field coding in the spacetime frequency domain

Martin Vetterli, Francisco Pereira Correia Pinto

We present a new method for compressing spatio-temporal audio data for reproduction through Wave Field Synthesis. The data is obtained by sampling the sound field in space at equally-spaced points on a straight line, and transformed into the frequency doma ...

2008

Sparse Sampling of Signal Innovations

Martin Vetterli, Pina Marziliano, Thierry Blu, Lionel Coulot, Pier Luigi Dragotti

Sparse sampling of continuous-time sparse signals is addressed. In particular, it is shown that sampling at the rate of innovation is possible, in some sense applying Occam's razor to the sampling of sparse signals. The noisy case is analyzed and solved, p ...

Institute of Electrical and Electronics Engineers2008