arXiv research

Sparse Hopfield model improves memory retrieval with fewer connections.

New quantum code lacks sparse lift.

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

This work introduces a method to compare sparse neural network topologies using graph theory.

Sparse-RS framework efficiently attacks models with sparse perturbations.

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…

Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on regular parallel hardware such as TPU. This inefficiency leads to poor/no performance benefits for s…

Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.

In compressed sensing, we wish to reconstruct a sparse signal $x$ from observed data $y$. In sparse coding, on the other hand, we wish to find a representation of an observed signal $y$ as a sparse linear combination, with coefficients $x$, of elements from an overcomplete dictionary. While many algorithms are competit…

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…

Dynamic Sparse Training finds efficient sparse networks from scratch.

Sparse group Lasso optimizes sparse and grouped parameters in high-dimensional data.

New method for hyperparameter tuning in sparse matrix factorization.

We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to…

High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a {\em regularization factor}, and solve them by the parametric simplex method (PSM). O…

This paper explores loss landscapes of sparse neural networks, finding unique characteristics compared to dense networks.

Sparse APCA identifies sparse factors in financial returns over time.

Study confirms sparse coding in whole brain using MRI data.

A new method for sparse PCA using orthogonal rotations and soft-thresholding.

Develops sparse portfolio strategy for high-dimensional assets.

Picasso is a new library for sparse learning problems in R and Python.

The CUR decomposition provides an approximation of a matrix $X$ that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of $X$. In this regard, it appears to be similar to many sparse PCA methods. However, CUR takes a randomized algorithm…

DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.

A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in…

Guarantees sparse recovery for neural networks with iterative hard thresholding.

New methods show sparse portfolios offer no advantage over mean-variance in diversification.

Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most…

msPCA solves sparse PCA for multiple components efficiently.

We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…

Predictive Sparse Manifold Transform learns dynamic video sequences.

New method finds sparse networks without labels, improving performance.

Sparse curves on surfaces grow at a specific intermediate rate.

Paper optimizes Laplacian regularization for sparse network clustering.

Is it possible to find the sparsest vector (direction) in a generic subspace $\mathcal{S} \subseteq \mathbb{R}^p$ with $\mathrm{dim}(\mathcal{S})= n < p$? This problem can be considered a homogeneous variant of the sparse recovery problem, and finds connections to sparse dictionary learning, sparse PCA, and many other …

This paper establishes conditions for sparse signal recovery with sparse measurements.

New method improves robust sparse association estimation.

We present a robust alternative to principal component analysis (PCA) --- called elliptical component analysis (ECA) --- for analyzing high dimensional, elliptically distributed data. ECA estimates the eigenspace of the covariance matrix of the elliptical data. To cope with heavy-tailed elliptical distributions, a mult…

We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal splitting method for the iterative solution of hierarchical sparse coding problem…

New measure SEV shows non-sparse models can still have low decision sparsity.

IDS improves sparse linear bandits by balancing information and regret.

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to …

PopArt efficiently solves sparse linear bandits with tighter recovery guarantees.

New method learns sparse distributions by thresholding samples, improving performance and efficiency.

We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = Φx_0+w$. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sp…

The paper improves lower bounds on deep neural network trajectories.

IHT improves sparse distribution learning.

Paper tackles NAS problem by modeling it as a sparse supernet.

New GPU kernels boost deep learning speed and memory efficiency.