Missing data is an important challenge when dealing with high dimensional data arranged in the form of an array. In this paper, we propose methods for estimation of the parameters of array variate normal probability model from partially observed multiway data. The methods developed here are useful for missing data impu…
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48 results for “high-dimensional arrays”
Rank-R FNN handles high-dimensional data efficiently.
problem Handling irregularities in high-dimensional data.
method Imposes Canonical/Polyadic decomposition on parameters.
result Achieves state-of-the-art performance on higher-order tensor data.
Locality-sensitive hashing converts high-dimensional feature vectors, such as image and speech, into bit arrays and allows high-speed similarity calculation with the Hamming distance. There is a hashing scheme that maps feature vectors to bit arrays depending on the signs of the inner products between feature vectors a…
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
The paper presents a new tensor sparsification method that reduces sample complexity for tensor approximation.
problem Efficiently approximating high-dimensional tensors with reduced sample size.
method Proposes a novel tensor sparsification algorithm to retain a subset of tensor entries.
result Achieves a given level of approximation accuracy with a much smaller sample complexity.
Developed a multiway classification method for sparse data.
problem Classification of multiway arrays with sparsity.
method Extended Distance Weighted Discrimination (DWD) to multiway context, accounting for sparsity.
result Improves classification accuracy in multiway structured data.
Analog arrays speed up ConvNets by parallelizing kernel matrix training.
problem Early ConvNets struggle with analog arrays due to small kernel matrices.
method Replicate kernel matrix on multiple analog arrays, training in parallel.
result Analog arrays achieve high acceleration factors (16-128) for ConvNets.
System for automatic differentiation in a functional array-processing language.
problem Efficient automatic differentiation in functional array languages.
method Automatic differentiation in a higher-order functional array-processing language with source-to-source support and global optimizations.
result The system outperforms state-of-the-art tools on machine learning and computer vision benchmarks.
Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regressionstat.ML
Improves magnetic field mapping using an array of magnetometers with noisy input.
problem Improving magnetic field maps in indoor environments with noisy magnetometer data.
method Uses Gaussian process regression with an array of magnetometers, incorporating known array positions and relative magnetometer locations.
result The method produces higher quality magnetic field maps compared to using a single magnetometer.
Tensor analysis tackles complex multidimensional data across fields.
problem Efficiently extracting information from high-dimensional data.
method Interdisciplinary approach combining statistics, optimization, and numerical linear algebra.
result Significant progress in tensor analysis over the last decade.
Simpler proof for non-basic sets in 2D.
problem Proving non-basic sets in 2D.
method Defining Sternfeld arrays and proving non-basic sets.
result Simpler proof of non-basic sets in 2D.
Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrayseess.SP
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
problem Challenges in DOA estimation with limited snapshots in sparse linear arrays.
method Introduces a Siamese neural network with a sparse augmentation layer for enhanced signal feature embedding.
result Demonstrates improved DOA estimation accuracy in sparse arrays.
Survey and benchmark high-dimensional Bayesian optimization of discrete sequences.
problem Heterogeneous experimental set-ups and technical barriers in high-dimensional Bayesian optimization of discrete sequences.
method Unified framework and software libraries to test and benchmark methods.
result Unified framework and software libraries for testing and benchmarking high-dimensional Bayesian optimization methods.
Develops a new tensor classification method for high-dimensional data.
problem Efficient learning algorithms exploiting tensorial structure in high-dimensional multi-way arrays.
method Tensor Train Multi-way Multi-level Kernel (TT-MMK) combining Canonical Polyadic decomposition, Dual Structure-preserving Support Vector Machine, and Tensor Train approximation.
result The TT-MMK method provides higher prediction accuracy and is more reliable computationally compared to other techniques.
Paper predicts spatial variation data from few samples using tensor methods.
problem Predict spatial variation data from limited samples in high-dimensional data.
method Bayesian tensor completion exploiting hidden low-rank property.
result Predicts spatial variation data efficiently from few samples.
Cognitive radar selects optimal antenna subarrays using deep learning.
problem Optimize radar antenna selection for cost and performance.
method Convolutional Neural Network (CNN) for multi-class classification.
result CNN provides 22% better classification performance and 72% more accurate DoA estimates.
Unified Bayesian framework for PTA data analysis tackles hierarchical model issues.
problem Hierarchical Bayesian modeling challenges in PTA data analysis.
method Reparameterization strategy using Normalizing Flows (NFs) and i-nessai nested sampler.
result Improved statistical robustness and computational efficiency in PTA analysis.
Novel CNN array for sign language recognition using wearable IMUs.
problem Efficiently recognizing sign language from wearable IMU signals.
method Two-dimensional Convolutional Neural Network array architecture for Indian sign language recognition.
result Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved.
The CHAMPION study clusters multi-dimensional accelerometer data to understand health links.
problem Clustering multi-dimensional data from pediatric longitudinal studies.
method Developed a finite mixture of multidimensional arrays model for clustering 4-dimensional accelerometer data.
result Demonstrated the feasibility and utility of clustering higher order data.
New distributed EnKF method for non-sequential assimilation of large datasets.
problem Computational intensity and order dependencies in traditional EnKF.
method Distributed computing for full model error covariance matrix.
result Non-sequential assimilation outperforms sequential in performance.
High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for l…
The paper examines extreme value statistics of high-dimensional sample covariances, with applications in finance and image analysis.
problem Statistical validation of normal conditions in high-dimensional time series data.
method Generalizes the maximal deviation of sample autocovariances to high dimensions and applies Gumbel-type extreme value asymptotics.
result Gumbel-type extreme value asymptotics holds true for high-dimensional sample covariances.
This paper improves uncertainty characterization in neural networks by learning latent representations.
problem Challenges in quantifying uncertainty in high-dimensional neural network parameter spaces.
method Introduces a variational inference framework for Bayesian neural networks that encodes complex distributions in a low-dimensional latent space.
result Improves uncertainty characterization and model generalization compared to methods working directly in the parameter space.
In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors…
Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Librariesstat.ML
We consider -way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces -rank appr…
New algorithm eliminates symmetry requirement for training neural networks on resistive device arrays.
problem Training accuracy on resistive device arrays depends on device switching symmetry.
method Developed 'Tiki-Taka' algorithm to minimize unintentional cost term due to device asymmetry.
result Achieves same accuracy with non-symmetric devices as with symmetric devices.
This paper improves neural network efficiency by combining filter columns and retraining, boosting array utilization and accuracy.
problem Efficient implementation of sparse convolutional neural networks on systolic arrays.
method Column combining of filter matrices, retraining of remaining weights, joint optimization for high utilization and accuracy.
result Significantly increased systolic array utilization efficiency (e.g., ~4x) and maintained high classification accuracy.
A neural network, IHT-Net, improves DOA estimation with sparse arrays.
problem Single-snapshot DOA estimation with sparse arrays in dynamic settings.
method IHT-inspired neural network with recurrent neural network and autoencoders.
result IHT-Net achieves faster convergence and higher accuracy in DOA estimation.
Myia compiler optimizes ML models with efficient AD for array programming.
problem Efficient automatic differentiation for array programming in ML.
method Introduces a new graph-based IR that supports function calls, higher-order functions, and recursion.
result Myia compiler enables efficient AD using source transformation without a tape, supporting higher-order derivatives.
Robust STAP with coprime arrays reduces clutter using sparse modeling.
problem Limited performance due to training samples support in practical applications.
method Two-stage approach: 1) RD virtual snapshot, 2) RD sparse measurement modeling with OMP-like recovery.
result Robust to prior knowledge errors, good clutter suppression performance.
Tangent automates derivatives in Python, improving expressiveness and performance.
problem Efficiently calculating derivatives for complex models in Python.
method Source-code transformation for dynamically typed array programming.
result Demonstrates improved expressiveness and performance in automatic differentiation.
Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.
problem One-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario.
method Formulated off-grid DOA estimation model, used Sparse Bayesian framework, proposed Learning-based Sparse Bayesian approach.
result Improved computational efficiency and accuracy in off-grid DOA estimation.
DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elementscs.LG
Infinite Tucker Decomposition (InfTucker) and random function prior models, as nonparametric Bayesian models on infinite exchangeable arrays, are more powerful models than widely-used multilinear factorization methods including Tucker and PARAFAC decomposition, (partly) due to their capability of modeling nonlinear rel…
We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, with only a twofold increase in run time. This is the first method which can exploit directional inform…
NLM combines neural networks and logic programming for complex reasoning.
problem Complex reasoning tasks involving logic and properties.
method Neural-symbolic architecture combining neural networks and logic programming.
result NLM achieves perfect generalization on various tasks.
Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-da…
Deep neural networks improve angle of arrival estimation with lower complexity.
problem Estimating the number of sources and their angles of arrival from a single antenna array observation.
method Apply a deep neural network (DNN) approach to the problem.
result Deep neural networks can attain maximum likelihood performance with feasible complexity and outperform other methods.
This paper proposes a learning framework for n-bit quantized neural networks that improves accuracy and speed on FPGAs.
problem Efficiently implementing quantized neural networks on FPGAs to maintain accuracy and speed.
method A novel learning framework for n-bit QNNs, constrained weights, reconstructed gradient function, n-BQ-NN structure, and SVPE array.
result Quantized models achieve almost the same accuracy as full-precision models and outperform typical low-precision QNNs.
In this paper we prove the following geometric inequality in the hyperbolic space $\H^n$ (, which is a hyperbolic Alexandrov-Fenchel inequality, \[\begin{array}{rcl} \ds \int_Σ\s_4 d μ\ge \ds\vs C_{n-1}^4ω_{n-1}\left\{\left(\frac{|Σ|}{ω_{n-1}} \right)^\frac 12 + \left(\frac{|Σ|}{ω_{n-1}} \right)^{\frac 12\frac…
The paper proves existence of solutions for mean field equations on compact Riemann surfaces.
problem Existence of solutions for mean field equations on compact Riemann surfaces.
method Min-max scheme introduced by Djadli-Malchiodi (2006) and Djadli (2008).
result Proves existence of solutions for mean field equations on compact Riemann surfaces.
Differentiable sorting framework using optimal transport.
problem Piecewise constant sorting function without gradient information.
method Linking sorting to optimal transport, adding entropic regularization, and approximating with Sinkhorn iterations.
result Differentiable sorting operators (S-sorts, S-CDFs, S-quantiles) for machine learning applications.
Eigenvalue problems for Laplacian in specific domains are studied, showing maximum eigenvalues occur under certain conditions.
problem Eigenvalue problems for Laplacian in doubly connected domains and geodesically symmetric spaces.
method Analytical study of boundary value problems for Laplacian.
result Maximum eigenvalues occur when domains are concentric or geodesic balls.
Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biolo…
In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such…
This paper introduces an efficient method for optimizing deep learning hyperparameters.
problem The high dependency of deep learning algorithms on hyper-parameters.
method Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning.
result The proposed OATM method significantly saves tuning time compared to state-of-the-art methods.
A proof of Guo-Wang's conjecture on the uniqueness of positive harmonic functions in the unit ballmath.AP
This paper proves a conjecture about unique positive harmonic functions in a ball.
problem Proving the uniqueness of positive harmonic functions in a unit ball for specific parameters.
method Analyzing a partial differential equation to show the solution is constant.
result Guo-Wang's conjecture is proven for the specified parameters.
Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions. To allevia…
We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional---2D---arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the norm and the pair-wise norm, which is convex but non-d…