pMMF is a parallel algorithm for matrix computation.
problem Finding multiscale structure and wavelets on matrices.
method pMMF is a parallel algorithm for MMF factorization.
result pMMF scales linearly in the dimension for sparse matrices.
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48 results for “Multiresolution Matrix Factorization”
Extends MMF to nonsymmetric matrices for hierarchical structure.
problem Capturing hierarchical structure in nonsymmetric matrices.
method Multiresolution Matrix Factorization (MMF) extended to nonsymmetric matrices.
result Effective for matrix compression tasks, outperforming low-rank methods.
New algorithm uncovers hierarchical block structure in large matrices.
problem Uncovering hierarchical block structure in symmetric matrices.
method Incremental multiresolution matrix factorization.
result Algorithm scales well to large matrices and uncovers structure one feature at a time.
MRTL learns interpretable spatial patterns efficiently.
problem Efficient and interpretable spatial analysis in various fields.
method Multiresolution Tensor Learning (MRTL) algorithm.
result 4~5x speedup with accurate and interpretable latent factors.
MKA improves Gaussian process regression for large datasets.
problem Gaussian process regression struggles with large datasets.
method MKA is a memory-efficient, direct kernel approximation method.
result MKA achieves better performance with small kernel length scales.
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP…
Cisco introduces a new time series model for better forecasting.
problem Improving time series forecasting accuracy.
method Developed a new multiresolution decoder-only model trained on large datasets.
result The new model achieves superior performance on observability datasets.
NAOMI improves imputation accuracy for long-range sequences.
problem Missing value imputation in spatiotemporal data.
method Non-autoregressive deep generative model exploiting multiresolution structure.
result Significant improvement in imputation accuracy (60% reduction in average prediction error).
AV-ASR system improves speech recognition with visual context.
problem Improving speech recognition accuracy with visual information.
method Transformer-based architecture with multiresolution and multimodal training.
result Multiresolution training speeds up convergence and improves WER by 18%.
On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learningcs.LG
Paper proposes a new adaptive multiscale value function approximation for reinforcement learning.
problem Value function approximation in reinforcement learning with varying complexity.
method Adaptive multiscale approximation using multiresolution analysis and tree approximation.
result Convergence rate of the multiscale approximation is independent of basis function regularity.
Combining neural networks and multiscale decomposition for financial market analysis.
problem Financial markets' complexity and mainstream models' limitations in capturing non-linear structures.
method Neural networks for non-linear associations combined with multiscale decomposition.
result Improved understanding of financial market data substructures.
Multiresolution RNN improves dialogue response generation.
problem Generating relevant and on-topic responses in dialogue systems.
method Introducing a multiresolution recurrent neural network that models natural language generation as two parallel sequences.
result The model outperforms competing approaches in dialogue response generation on the Ubuntu domain and appears more relevant on Twitter.
MathNet uses wavelets for graph representation and learning.
problem Graph Neural Networks (GNNs) for graph classification and regression.
method Multiresolution Haar-like wavelets, graph convolution, and pooling.
result MathNet achieves notable accuracy gains on graph classification and regression tasks.
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraintsstat.ML
This paper approximates scattered data using samplet coordinates with sparsity constraints.
problem Scattered data approximation with sparsity constraints.
method Samplet basis pursuit with -regularization, multiresolution techniques, and semi-smooth Newton method.
result The proposed method provides faster convergence and better signal sparsity compared to existing methods.
A new multigrid method tackles PDEs with rough coefficients efficiently.
problem Solving PDEs with coefficients that are only -bounded.
method A multiresolution operator decomposition using a game theory formulation.
result The method achieves near-linear complexity and rigorous accuracy.
New model captures long-range patterns in sequences efficiently.
problem Efficiently capturing long-range patterns in sequential data.
method Inspired by wavelet multiresolution analysis, introduces MultiresLayer with multiresolution convolution.
result State-of-the-art performance on sequence classification and autoregressive density estimation tasks.
Transformer models outperform recurrent ones in modeling hierarchical data.
problem Modeling hierarchical structure in data.
method Introducing Multiresolution Transformer Networks leveraging self-attention.
result Multiresolution Transformer Networks significantly outperform state-of-the-art models on query suggestion datasets.
The study develops methods to summarize team passing strategies from soccer data.
problem Modeling spatial passing networks across multiple games with varying positions.
method Multiresolution tensor decomposition and Poisson nonnegative block term decomposition.
result Automatic production of network motifs at different levels of detail.
In this letter we exhibit the relation between the isometries of a Riemannian contraction of a sub-Riemannian manifold and those of the sub-Riemannian metric, for to use this relation with two goals: establishing a result about the existence of fixed points of isometries groups; and the other, defining a Multiresolutio…
New model improves GP approximations by relaxing independence across resolutions.
problem Overfitting and non-smooth predictions in multiresolution GPs.
method Conditional independence among GPs across resolutions.
result Improved robustness against overfitting and smoother predictions.
Optimizes over flag manifolds for numerical PDE and statistics.
problem Optimizing over flag manifolds for numerical PDE and statistics.
method Develops tools for Riemannian optimization on flag manifolds, deriving analytic expressions and parameterizations.
result Closed-form analytic expressions and parameterizations for various geometric objects on flag manifolds.
Wavelet Kolmogorov-Arnold Networks improve federated learning performance.
problem Improving performance in federated learning with heterogeneous data.
method Implemented Wav-KAN with CWT and DWT for multiresolution capability, integrating wavelet-based activation functions.
result Significant improvements in computational efficiency, robustness, and accuracy in federated learning.
NIMFA is a Python library for nonnegative matrix factorization.
problem Efficiently factorizing nonnegative matrices for various applications.
method Unified interface, state-of-the-art methods, initialization approaches, quality scoring, supports dense and sparse matrices.
result Unified and efficient implementation of nonnegative matrix factorization methods.
Online algorithm for matrix factorization using Broyden updates.
problem Efficiently compute matrix factorizations with online data.
method Low-rank updates to dictionary matrix, derived from a simple objective function.
result Demonstrated efficiency on real dataset compared to NMF.
A new matrix factorization method that approximates data without requiring nonnegativity or convexity.
problem Approximating data matrices without the constraints of nonnegativity or convexity.
method A multi-objective optimization problem finds conical combinations of templates that approximate a given data matrix.
result The method allows for approximation of data sets without the usual constraints of nonnegativity or convexity.
New method for hyperparameter tuning in sparse matrix factorization.
problem Hyperparameter tuning in sparse matrix factorization.
method Numerical method based on evaluating the zero point of normalization factor in sparse matrix prior.
result Our method outperforms existing algorithms in ground-truth sparse matrix reconstruction.
New method combines simulated annealing and Levy distribution for fast matrix factorization.
problem High complexity and difficulty in parallelizing matrix factorization for large matrices.
method Combining simulated annealing with Levy distribution for matrix factorization.
result Achieves good solutions in acceptable time with low computations.
Gradient descent proves global convergence for 4-layer matrix factorization.
problem Global convergence of gradient descent on four-layer matrix factorization under random initialization.
method New techniques to show saddle-avoidance properties and extend eigenvalue theories.
result Polynomial-time global convergence guarantee for randomly initialized gradient descent on four-layer matrix factorization.
Unified framework for nonconvex matrix completion with linearly parameterized factors.
problem Matrix completion with improved accuracy using linearly parameterized factors.
method Unified nonconvex optimization framework with Correlated Parametric Factorization condition.
result Uniform upper bounds for low-rank estimation at any local minimum.
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
Proposes expectile matrix factorization for skewed data analysis.
problem Skewed and extreme data cannot be explained by least squares-based matrix factorization.
method Introduces asymmetric least squares into matrix factorization framework and proposes an efficient algorithm.
result The proposed scheme achieves lower recovery errors than least squares-based methods in synthetic and real-world data.
Paper restricts non-negative matrix factorization to stochastic matrices for data analysis.
problem Analyzing unstructured data like topic models and face storage retrieval.
method Necessary and sufficient conditions for unique factorization, natural bounds on parameters, consistent estimator.
result Unique factorization conditions and parameter bounds for observed data.
Proposes a robust factor analysis for matrix data.
problem Robust factor analysis for matrix data with heavy-tailed or contaminated data.
method Bilinear factor analysis based on the matrix-variate distribution.
result Significantly higher breakdown point than traditional methods.
Federated multi-view matrix factorization learns from multiple data sources without centralizing user data.
problem Cold-start federated recommendations and multi-view data structure.
method Federated learning framework extended to multi-view matrix factorization.
result Federated multi-view matrix factorization outperforms simpler methods in cold-start federated recommendations.
Algorithm for fast matrix factorization of large datasets.
problem Factorizing huge matrices with sparse or dense factors.
method Subsampling and iterative learning of matrix factors.
result Significant speed-ups on large datasets.
Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in low-dimensional continuous space. In particular, mixtures of Gaussians can be fitted to data very quickly using an accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999). In thi…
We want to construct a homological link invariant whose Euler characteristic is MOY polynomial as Khovanov and Rozansky constructed a categorification of HOMFLY polynomial. The present paper gives the first step to construct a categorification of MOY polynomial. For the essential colored planar diagrams with additional…
Develops a fast algorithm for fitting multilevel factor models.
problem Fitting multilevel factor models with covariance structure.
method Novel expectation-maximization algorithm tailored for multilevel factor models.
result Shows efficient computation of inverse of positive definite MLR matrix.
Graph neural networks speed up nonnegative matrix factorization.
problem Efficiently factorize nonnegative matrices for various applications.
method Developed a graph neural network that combines bipartite self-attention with ADMM updates.
result Significant acceleration achieved in nonnegative matrix factorization.
k-means clustering is shown to be equivalent to matrix factorization.
problem Clustering data points into k clusters.
method Expressed k-means as the Frobenius norm of a data matrix and its low-rank approximation.
result k-means is a matrix factorization problem.
Gradient descent on matrix factorization leads to minimum nuclear norm solution.
problem Optimizing underdetermined quadratic objectives over matrices.
method Gradient descent on a full dimensional factorization of the matrix.
result Gradient descent converges to the minimum nuclear norm solution.
Paper proposes algorithms for BMF using integer programming.
problem Approximating binary input matrix as product of two smaller binary factors.
method Alternating optimization strategy using integer programming to solve subproblems and combine solutions.
result Proposed algorithms outperform state of the art on medium-scale problems.
New nonconvex regularizers improve low-rank matrix recovery efficiency and accuracy.
problem Efficiently recover low-rank matrices from incomplete data.
method Factor group-sparse regularization, related to Schatten-p norms.
result Improved generalization error bounds for Schatten-p norms as p decreases.
New NMF algorithm uses Toeplitz matrix for facial recognition.
problem Facial recognition performance improvement.
method Proposes TNMF algorithm with Toeplitz penalty for NMF.
result TNMF outperforms ZNMF and other constrained NMF algorithms.
Introduces BMF for efficient matrix factorization of large data.
problem Efficiently factorizing large scale matrices with limited memory.
method Uses block matrix approach and factorization at a block level.
result Demonstrates faster convergence on large matrices.
Global optimization algorithm finds sparse mixed membership matrix factorization's global optimum.
problem Sparse mixed membership matrix factorization problems with local optima.
method Derives a global optimization algorithm for sparse mixed membership matrix factorization.
result Guaranteed -global optimum across random initializations and multiple modes.
BLC enhances privacy in group-based recommender systems.
problem Privacy in group-based recommendation systems.
method Automatic group learning and novel matrix factorization.
result Privacy-enhanced recommendations with no accuracy loss.
Predict artist efficiency in VFX shots using matrix completion.
problem Predicting artist efficiency in rendering VFX shots.
method Structured matrix factorization models for bounded entries.
result Effective models for predicting artist efficiency.