#### Filter Results:

- Full text PDF available (29)

#### Publication Year

2007

2017

- This year (1)
- Last 5 years (24)
- Last 10 years (33)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Data Set Used

#### Key Phrases

#### Method

Learn More

- Ignacio Ramírez, Pablo Sprechmann, Guillermo Sapiro
- 2010 IEEE Computer Society Conference on Computer…
- 2010

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are… (More)

- Pablo Sprechmann, Ignacio Ramírez, Guillermo Sapiro, Yonina C. Eldar
- IEEE Transactions on Signal Processing
- 2011

Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an <i>l</i><sub>1</sub>-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso at the individual feature level, with… (More)

- Pablo Sprechmann, Guillermo Sapiro
- 2010 IEEE International Conference on Acoustics…
- 2010

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are… (More)

- Jonathan Pokrass, Alexander M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
- Comput. Graph. Forum
- 2013

We present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. We show… (More)

In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing. One of the plights of existing hashing techniques is the intrinsic trade-off between performance and computational complexity: while longer hash codes allow for lower false positive rates, it is very difficult to increase the… (More)

- Joan Bruna, Pablo Sprechmann, Yann LeCun
- ArXiv
- 2015

Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its lowresolution corrupted observation. When the scaling ratio is small, point estimates achieve impressive performance, but… (More)

- Pablo Sprechmann, Ignacio Ramírez, Guillermo Sapiro, Yonina C. Eldar
- 2010 44th Annual Conference on Information…
- 2010

Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is done by solving an ℓ<inf>1</inf>-regularized linear regression problem, usually called Lasso. In this work we first combine the sparsity-inducing property of the Lasso model, at the individual feature level, with the block-sparsity… (More)

Traditionally, NMF algorithms consist of two separate stages: a training stage, in which a generative model is learned; and a testing stage in which the pre-learned model is used in a high level task such as enhancement, separation, or classification. As an alternative, we propose a task-supervised NMF method for the adaptation of the basis spectra learned… (More)

- Pablo Sprechmann, Ignacio Ramírez, Pablo Cancela, Guillermo Sapiro
- 2011 IEEE International Conference on Acoustics…
- 2011

A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely… (More)

We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only… (More)