# Formation matrix

## Papers overview

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Highly Cited

2012

Highly Cited

2012

- AISTATS
- 2012

We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on averageâ€¦Â (More)

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Highly Cited

2012

Highly Cited

2012

- ICML
- 2012

In this paper we address the following question: â€œCan we approximately sample from a Bayesian posterior distribution if we areâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

- 2010

We propose a class of observation driven time series models called generalized autoregressive score models for which the scaledâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- IEEE Transactions on Communications
- 2009

In this work, we consider the two-way relay network (TWRN) where two terminals exchange their information through a relay node inâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- IEEE Transactions on Robotics
- 2008

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localizationâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- IEEE Transactions on Vehicular Technology
- 2006

Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MSâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- 2004

This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that isâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- 2000

We introduce a Lagrange Multiplier (LM) test for the constant-correlation hypothesis in a multivariate GARCH model. The testâ€¦Â (More)

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Highly Cited

1998

Highly Cited

1998

- IEEE Signal Processing Letters
- 1998

This paper presents a simple expression for the Cramer-Rao bound (CRB) for parametric estimation under differentiableâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- Machine Learning
- 1997

We discuss Bayesian methods for model averaging and model selection among Bayesian-network models with hidden variables. Inâ€¦Â (More)

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