Sparse coding schemes are employed by many sensory systems and implement efficient coding principles. Yet, the computations yielding sparse representations are often only partly understood. The early auditory system of the grasshopper produces a temporally and population-sparse representation of natural communication signals. To reveal the computations generating such a code, we estimated 1D an...

Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning appr...

We introduce a growing network model in which a new node attaches to a randomly selected node, as well as to all ancestors of the target node. This mechanism produces a sparse, ultrasmall network where the average node degree grows logarithmically with network size while the network diameter equals 2. We determine basic geometrical network properties, such as the size dependence of the number o...

Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov’s accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting). Substantial numerical evidence is provided on both synth...

Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We sh...

This paper investigates the relationship between the loss function, the type of regularization, and the resulting model sparsity of discriminatively-trained multiclass linear models. The effects on sparsity of optimizing log loss are straightforward: L2 regularization produces very dense models while L1 regularization produces much sparser models. However, optimizing hinge loss yields more nuan...

In this paper we investigate two discriminative classification approaches for frame-based speaker identification and verification, namely Support Vector Machine (SVM) and Sparse Kernel Logistic Regression (SKLR). SVMs have already shown good results in regression and classification in several fields of pattern recognition as well as in continuous speech recognition. While the non-probabilistic ...