Search Results for author: Henrik Ohlsson

Found 8 papers, 1 papers with code

Expert-guided Regularization via Distance Metric Learning

no code implementations9 Dec 2019 Shouvik Mani, Mehdi Maasoumy, Sina Pakazad, Henrik Ohlsson

As an alternative to standard regularization techniques, we propose Distance Metric Learning Regularization (DMLreg), an approach for eliciting prior knowledge from domain experts and integrating that knowledge into a regularized linear model.

Metric Learning

Sparse phase retrieval via group-sparse optimization

no code implementations24 Feb 2014 Fabien Lauer, Henrik Ohlsson

This paper deals with sparse phase retrieval, i. e., the problem of estimating a vector from quadratic measurements under the assumption that few components are nonzero.

Retrieval

Finding sparse solutions of systems of polynomial equations via group-sparsity optimization

no code implementations22 Nov 2013 Fabien Lauer, Henrik Ohlsson

In particular, we show how these solutions can be recovered from group-sparse solutions of a derived system of linear equations.

Scalable Anomaly Detection in Large Homogenous Populations

no code implementations20 Sep 2013 Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, S. Shankar Sastry

The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests.

Anomaly Detection Combinatorial Optimization

Compressive Shift Retrieval

no code implementations20 Mar 2013 Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry

The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.

Compressive Sensing Retrieval

CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem

no code implementations NeurIPS 2012 Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry

This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal.

Compressive Sensing Retrieval

A Probabilistic Perspective on Gaussian Filtering and Smoothing

1 code implementation10 Jun 2010 Marc Peter Deisenroth, Henrik Ohlsson

We present a general probabilistic perspective on Gaussian filtering and smoothing.

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