We consider the problem of building shift-invariant representations for long signals in the context of distributed processing. We propose an asynchronous algorithm based on coordinate descent called DICOD to efficiently solve the `1minimization problems involved in convolutional sparse coding. This algorithm leverages the weak temporal dependency of the convolution to reduce the interprocess co...

Bag-of-words document representations are often used in text, image and video processing. While it is relatively easy to determine a suitable word dictionary for text documents, there is no simple mapping from raw images or videos to dictionary terms. The classical approach builds a dictionary using vector quantization over a large set of useful visual descriptors extracted from a training set,...

1.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 The stabilized auditory image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

A fundamental understanding of the relationship between delay performance and complexity in network coding is instrumental towards its application in practical systems. The main argument against delay-optimal random linear network coding (RLNC) is its decoding complexity, which is O(n) for n original packets. Fountain codes, such as LT and Raptor codes, reduce the decoding load on the receiver ...

Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization. We then give a simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components...

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of...

Prior work has shown that features which appear to be biologically plausible as well as empirically useful can be found by sparse coding with a prior such as a laplacian (L1) that promotes sparsity. We show how smoother priors can preserve the benefits of these sparse priors while adding stability to the Maximum A-Posteriori (MAP) estimate that makes it more useful for prediction problems. Addi...

Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors (i.e., dictionary), has been widely applied in machine learning, signal processing and neuroscience. Recently, one specific SC technique, Group Sparse Coding (GSC), has been proposed to learn a common dictionary over multiple different groups of data, where the data groups are assumed to be pre-de...

Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data into a significantly higherdimensional space with sparse coding can lead to superior classification performance. However, computationally it is challenging to learn a set of highly over-complete dictionary bases and ...