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- Pierre Garrigues, Bruno A. Olshausen
- NIPS
- 2010

We propose a class of sparse coding models that utilizes a Laplacian Scale Mixture (LSM) prior to model dependencies among coefficients. Each coefficient is modeled as a Laplacian distribution with a variable scale parameter, with a Gamma distribution prior over the scale parameter. We show that, due to the conjugacy of the Gamma prior, it is possible to… (More)

- Pierre Garrigues, Bruno A. Olshausen
- NIPS
- 2007

It has been shown that adapting a dictionary of basis functions to the statistics of natural images so as to maximize sparsity in the coefficients results in a set of dictionary elements whose spatial properties resemble those of V1 (primary visual cortex) receptive fields. However, the resulting sparse coefficients still exhibit pronounced statistical… (More)

- Pierre Garrigues, Laurent El Ghaoui
- NIPS
- 2008

It has been shown that the problem of `1-penalized least-square regression commonly referred to as the Lasso or Basis Pursuit DeNoising leads to solutions that are sparse and therefore achieves model selection. We propose in this paper RecLasso, an algorithm to solve the Lasso with online (sequential) observations. We introduce an optimization problem that… (More)

- Adam S. Charles, Pierre Garrigues, Christopher J. Rozell
- Neural Computation
- 2012

The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide… (More)

- Pierre Garrigues, Sachin Farfade, Hamid Izadinia, Kofi Boakye, Yannis Kalantidis
- ACM Multimedia
- 2017

Automated photo tagging has established itself as one of the most compelling applications of deep learning. While deep convolutional neural networks have repeatedly demonstrated top performance on standard datasets for classification, there are a number of often overlooked but important considerations when deploying this technology in a real-world scenario.… (More)

- Pierre Garrigues, Avideh Zakhor
- VLBV
- 2005

In this paper, we propose a new scheme for position coding of the atoms within the Matching Pursuit algorithm as applied to the displaced frame difference (DFD) in a hybrid video encoder. We exploit the spatial and temporal coherence of the positions of the atoms in the DFD: the atoms tend to cluster in regions where the motion prediction fails. The… (More)

A common assumption in neuroscience is that the visual system is adapted to the statistics of natural images [1]. Hence, by building probabilistic models of natural images, we can gain insight into how visual information is represented and processed in the visual cortex. For example, it has been shown that learning a sparse code for images predicts the… (More)

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