arXiv research

CDSSD detects sparse changes in partially observable data streams.

Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.

Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we formally determine a proper regularization which is intimately related …

VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.

Sparse Gaussian Processes improve scalability by learning inducing points from data.

CNNs predict spatial fields from sparse data.

New method recovers sparse signals from nonlinear observations with robust error bounds.

We investigate the difficulties of training sparse neural networks and make new observations about optimization dynamics and the energy landscape within the sparse regime. Recent work of \citep{Gale2019, Liu2018} has shown that sparse ResNet-50 architectures trained on ImageNet-2012 dataset converge to solutions that a…

DDD reformulated for sparse matrices, integrating trajectory and snapshot time series data.

Meta-learn sparse Gaussian process inference for faster predictions.

Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possible clusters. This requires dense storage and runtime costs that scale with the total number of clusters, even though typically only a few clu…

Sparse deep learning improves prediction uncertainty for time series data.

In this paper, we consider the classic measurement error regression scenario in which our independent, or design, variables are observed with several sources of additive noise. We will show that our motivating example's replicated measurements on both the design and dependent variables may be leveraged to enhance a spa…

Efficiently estimates longitudinal networks by merging sparse networks.

Framework learns continuous dynamics from sparse trajectories.

Sparse modeling improves portfolio optimization by reducing errors in complex market systems.

We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independ…

Graph neural networks learn PDEs from sparse, irregular data.

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly used methods to overcome this limitation are 1) the variational sparse approximation which relies on inducing points and 2) the state-space…

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…

A new method selects inducing points to optimize high-throughput Bayesian optimisation.

Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.

Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we expl…

DFSOS improves sparse discriminant analysis for high-dimensional data.

The paper tackles tensor factorization and completion from noisy data.

Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawke…

LD-EnSF speeds up data assimilation with sparse observations.

SDIFT generates full-field dynamics from sparse, irregular data.

Develops a new point process model for detecting neural spike sequences.

Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to an unknown union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for complete data is by now well-un…

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance function parameters increases with the dimension. Even evaluation of the likelihood fu…

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BO…

Entropy regularization improves interpretability of probabilistic clustering models.

New sparse GP model learns compositional kernels efficiently.

New method upsamples sparse, non-uniform point clouds more accurately.

We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in ma…

Dynamic Sparse Training finds efficient sparse networks from scratch.

New method improves Gaussian process regression on complex, sparse point clouds.

Method predicts multistable system states from sparse measurements.

New method for hyperparameter tuning in sparse matrix factorization.

FLASH-MAX predicts electromagnetic fields from sparse data in seconds.

SJS model predicts label shifts in multinomial datasets.

Machine learning enhances ocean studies by analyzing sparse, multi-scale data.

New method combines spectral and sparse methods for Gaussian processes.

Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.

High-dimensional time series data exist in numerous areas such as finance, genomics, healthcare, and neuroscience. An unavoidable aspect of all such datasets is missing data, and dealing with this issue has been an important focus in statistics, control, and machine learning. In this work, we consider a high-dimensiona…

Method learns dynamics from sparse, irregular feature data.

Gaussian Processes improve data interpolation from diverse experiments.