{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/tensor-decomposition/papers/4","list_of":"/task/tensor-decomposition","task":"Tensor Decomposition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":7,"rows_per_page":100,"rows":[301,400],"of":618,"counts":{"archive_papers_tagged":618,"with_a_code_link":154,"where_syntology_ran_a_sample":32,"not_listed_spam_title":0,"listed":618,"listed_where_code_ran":32,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":28,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":28,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/tensor-decomposition","prev":"/task/tensor-decomposition/papers/3","next":"/task/tensor-decomposition/papers/5","papers":[{"url":null,"slug":"adaptive-embedding-for-temporal-network","title":"Efficient Estimation for Longitudinal Networks via Adaptive Merging","date":"2022-11-15","arxiv_id":"2211.07866","repositories_listed":0,"syntology":null},{"url":null,"slug":"average-case-complexity-of-tensor","title":"Average-Case Complexity of Tensor Decomposition for Low-Degree Polynomials","date":"2022-11-10","arxiv_id":"2211.05274","repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-bounds-for-the-convergence-of-tensor","title":"Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models","date":"2022-11-07","arxiv_id":"2211.03827","repositories_listed":0,"syntology":null},{"url":null,"slug":"stn-a-new-tensor-network-method-to-identify","title":"STN: a new tensor network method to identify stimulus category from brain activity pattern","date":"2022-10-31","arxiv_id":"2210.16993","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-regularized-tensor-regression-a-domain","title":"Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling","date":"2022-10-26","arxiv_id":"2211.05581","repositories_listed":0,"syntology":null},{"url":null,"slug":"tucker-o-minus-decomposition-for-multi-view","title":"Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering","date":"2022-10-23","arxiv_id":"2210.12638","repositories_listed":0,"syntology":null},{"url":null,"slug":"sekron-a-decomposition-method-supporting-many","title":"SeKron: A Decomposition Method Supporting Many Factorization Structures","date":"2022-10-12","arxiv_id":"2210.06299","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-and-mining-multi-aspect-graphs-with","title":"Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition","date":"2022-10-10","arxiv_id":"2210.04404","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-matrices-for-tensor-network","title":"Latent Matrices for Tensor Network Decomposition and to Tensor Completion","date":"2022-10-07","arxiv_id":"2210.03392","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensoranalyzer-identification-of-urban","title":"TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization","date":"2022-10-06","arxiv_id":"2210.02623","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-uterine-activity-from","title":"Estimating uterine activity from electrohysterogram measurements via statistical tensor decomposition","date":"2022-09-06","arxiv_id":"2209.02183","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-decomposition-based-personalized","title":"Tensor Decomposition based Personalized Federated Learning","date":"2022-08-27","arxiv_id":"2208.12959","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-nonnegative-tucker-decomposition-and","title":"Noisy Nonnegative Tucker Decomposition with Sparse Factors and Missing Data","date":"2022-08-17","arxiv_id":"2208.08287","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-low-rank-decomposition-for-real","title":"Approximate Real Symmetric Tensor Rank","date":"2022-07-25","arxiv_id":"2207.12529","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-programmable-memory-controller-for","title":"Towards Programmable Memory Controller for Tensor Decomposition","date":"2022-07-17","arxiv_id":"2207.08298","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-approximation-for-general-tensor","title":"Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions","date":"2022-07-15","arxiv_id":"2207.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-networks-in-machine-learning","title":"Tensor networks in machine learning","date":"2022-07-06","arxiv_id":"2207.02851","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-and-scalable-tensor-completion","title":"A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System","date":"2022-06-27","arxiv_id":"2206.13355","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconfigurable-intelligent-surface-aided-6g","title":"Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning","date":"2022-06-11","arxiv_id":"2206.05427","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-control-overhead-of-intelligent","title":"Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach","date":"2022-06-10","arxiv_id":"2206.05341","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-mri-using-learned-transform-based","title":"Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT$^2$LR-Net)","date":"2022-06-02","arxiv_id":"2206.00850","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-artifact-removal-from-eeg-recordings","title":"Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition","date":"2022-06-01","arxiv_id":"2206.00635","repositories_listed":0,"syntology":null},{"url":null,"slug":"stn-scalable-tensorizing-networks-via","title":"STN: Scalable Tensorizing Networks via Structure-Aware Training and Adaptive Compression","date":"2022-05-30","arxiv_id":"2205.15198","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-bayesian-learning-for-data","title":"Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling","date":"2022-05-28","arxiv_id":"2205.14283","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-efficient-gaussian-tensor-network","title":"Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs","date":"2022-05-26","arxiv_id":"2205.13163","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-order-pooling-for-graph-neural-networks","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","date":"2022-05-24","arxiv_id":"2205.11691","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-multivariate-functions-using-a","title":"Decoupling multivariate functions using a nonparametric filtered tensor decomposition","date":"2022-05-23","arxiv_id":"2205.11153","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-shape-search-for-optimum-data","title":"Tensor Shape Search for Optimum Data Compression","date":"2022-05-21","arxiv_id":"2205.10651","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-order-multilinear-discriminant-analysis","title":"High-Order Multilinear Discriminant Analysis via Order-$\\textit{n}$ Tensor Eigendecomposition","date":"2022-05-18","arxiv_id":"2205.09191","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-decompositions-for-hyperspectral-data","title":"Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review","date":"2022-05-13","arxiv_id":"2205.06407","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-structured-block-term-tensor","title":"Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing","date":"2022-05-08","arxiv_id":"2205.03798","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-polynomial-transformations","title":"Learning Polynomial Transformations","date":"2022-04-08","arxiv_id":"2204.04209","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeptensor-low-rank-tensor-decomposition-with","title":"DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors","date":"2022-04-07","arxiv_id":"2204.03145","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-label-correlations-for-second-order","title":"Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference","date":"2022-04-07","arxiv_id":"2204.03619","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-high-order-tensor-completion-algorithm","title":"A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization","date":"2022-04-04","arxiv_id":"2204.01732","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-channel-estimation-for-irs","title":"Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach","date":"2022-03-30","arxiv_id":"2203.16164","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-tensor-completion-via-low-rank-tensor","title":"Noisy Tensor Completion via Low-rank Tensor Ring","date":"2022-03-14","arxiv_id":"2203.08857","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-spectral-algorithm-for-overcomplete","title":"A Robust Spectral Algorithm for Overcomplete Tensor Decomposition","date":"2022-03-05","arxiv_id":"2203.02790","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-train-unstable-looped-tensor-network","title":"How to Train Unstable Looped Tensor Network","date":"2022-03-05","arxiv_id":"2203.02617","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-probability-estimation-using-tensor","title":"Joint Probability Estimation Using Tensor Decomposition and Dictionaries","date":"2022-03-03","arxiv_id":"2203.01667","repositories_listed":0,"syntology":null},{"url":null,"slug":"delta-family-approach-for-the-stochastic","title":"Delta family approach for the stochastic control problems of utility maximization","date":"2022-02-25","arxiv_id":"2202.12745","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-algorithm-for-overcomplete-order-3","title":"Fast algorithm for overcomplete order-3 tensor decomposition","date":"2022-02-14","arxiv_id":"2202.06442","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-coupled-cp-decomposition-for-principal","title":"Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA","date":"2022-02-09","arxiv_id":"2202.04719","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-emergence-of-convolutional","title":"Data-driven emergence of convolutional structure in neural networks","date":"2022-02-01","arxiv_id":"2202.00565","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-based-basis-function-learning-for","title":"Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields","date":"2022-01-21","arxiv_id":"2201.08583","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-imitation-learning-for-multi","title":"Conditional Imitation Learning for Multi-Agent Games","date":"2022-01-05","arxiv_id":"2201.01448","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-multiple-power-iteration-from","title":"Selective Multiple Power Iteration: from Tensor PCA to gradient-based exploration of landscapes","date":"2021-12-23","arxiv_id":"2112.12306","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-measure-of-model-redundancy-for","title":"A New Measure of Model Redundancy for Compressed Convolutional Neural Networks","date":"2021-12-09","arxiv_id":"2112.04857","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-tensor-decomposition-for-compression","title":"Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization","date":"2021-12-07","arxiv_id":"2112.03690","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tensor-btd-based-modulation-for-massive","title":"A Tensor-BTD-based Modulation for Massive Unsourced Random Access","date":"2021-12-05","arxiv_id":"2112.02629","repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-and-upper-bounds-on-the-pseudo","title":"Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"harmonic-retrieval-with-l-1-tucker-tensor","title":"Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition","date":"2021-11-29","arxiv_id":"2111.14780","repositories_listed":0,"syntology":null},{"url":null,"slug":"vec2node-self-training-with-tensor","title":"Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-parallel-tensor-decomposition-with","title":"A Fast Parallel Tensor Decomposition with Optimal Stochastic Gradient Descent: an Application in Structural Damage Identification","date":"2021-11-04","arxiv_id":"2111.02632","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-sparse-tensor-compression-for-neural","title":"Low-Rank+Sparse Tensor Compression for Neural Networks","date":"2021-11-02","arxiv_id":"2111.01697","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-filter-based-on-relations-for","title":"A Semantic Filter Based on Relations for Knowledge Graph Completion","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/landscape-analysis-of-an-improved-power","slug":"landscape-analysis-of-an-improved-power","title":"Landscape analysis of an improved power method for tensor decomposition","date":"2021-10-29","arxiv_id":"2110.15821","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/landscape-analysis-of-an-improved-power#ran","syntology_url":"https://syntology.ai/paper/2110.15821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15821"}},"official":null}},{"url":null,"slug":"applying-differential-privacy-to-tensor","title":"Applying Differential Privacy to Tensor Completion","date":"2021-10-01","arxiv_id":"2110.00539","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-completion-as-tensor","title":"Knowledge Graph Completion as Tensor Decomposition: A Genreal Form and Tensor N-rank Regularization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-mode-deep-matrix-and-tensor","title":"Multi-Mode Deep Matrix and Tensor Factorization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-simulated-to-visual-data-a-robust-low","title":"From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach using lp-Regression for Outlier Resistance","date":"2021-09-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconfigurable-low-latency-memory-system-for","title":"Reconfigurable Low-latency Memory System for Sparse Matricized Tensor Times Khatri-Rao Product on FPGA","date":"2021-09-18","arxiv_id":"2109.08874","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-tensor-network-representation-for-high","title":"Multi-Tensor Network Representation for High-Order Tensor Completion","date":"2021-09-09","arxiv_id":"2109.04022","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-network-embedding-via-tensor","title":"Temporal Network Embedding via Tensor Factorization","date":"2021-08-22","arxiv_id":"2108.09837","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-completion-using-geodesics-on-segre","title":"Tensor completion using geodesics on Segre manifolds","date":"2021-08-02","arxiv_id":"2108.00735","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-guided-sparse-tensor-based-model","title":"Attribute Guided Sparse Tensor-Based Model for Person Re-Identification","date":"2021-07-29","arxiv_id":"2108.04352","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-tensor-decomposition-based-1","title":"Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework","date":"2021-07-26","arxiv_id":"2107.12422","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-deep-learning-and-augmented","title":"Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments","date":"2021-07-23","arxiv_id":"2107.11043","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-low-rank-tensor-decomposition-by-ridge","title":"Fast Low-Rank Tensor Decomposition by Ridge Leverage Score Sampling","date":"2021-07-22","arxiv_id":"2107.10654","repositories_listed":0,"syntology":null},{"url":null,"slug":"canonical-polyadic-decomposition-and-deep","title":"Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection","date":"2021-07-20","arxiv_id":"2107.09519","repositories_listed":0,"syntology":null},{"url":null,"slug":"covid-19-multidimensional-kaggle-literature","title":"COVID-19 Multidimensional Kaggle Literature Organization","date":"2021-07-17","arxiv_id":"2107.08190","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-space-model-for-higher-order-networks","title":"Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition","date":"2021-06-30","arxiv_id":"2106.16042","repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-and-upper-bounds-on-the-vc-dimension-of","title":"Lower and Upper Bounds on the VC-Dimension of Tensor Network Models","date":"2021-06-22","arxiv_id":"2106.11827","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-learning-based-precoder-codebooks-for","title":"Tensor Learning-based Precoder Codebooks for FD-MIMO Systems","date":"2021-06-21","arxiv_id":"2106.11374","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-deflation-process-in-over","title":"Understanding Deflation Process in Over-parametrized Tensor Decomposition","date":"2021-06-11","arxiv_id":"2106.06573","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-decompose-a-tensor-with-group","title":"Algorithms from Invariants: Smoothed Analysis of Orbit Recovery over $SO(3)$","date":"2021-06-04","arxiv_id":"2106.02680","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-decomposition-for-learning-gaussian","title":"Tensor decomposition for learning Gaussian mixtures from moments","date":"2021-06-01","arxiv_id":"2106.00555","repositories_listed":0,"syntology":null},{"url":null,"slug":"dermoscopic-image-classification-with-neural","title":"Dermoscopic Image Classification with Neural Style Transfer","date":"2021-05-17","arxiv_id":"2105.07592","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-train-recurrent-neural-networks-for-1","title":"Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting","date":"2021-05-11","arxiv_id":"2105.04983","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-algorithms-for-low-rank","title":"Reconstruction Algorithms for Low-Rank Tensors and Depth-3 Multilinear Circuits","date":"2021-05-04","arxiv_id":"2105.01751","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-good-state-and-action","title":"Learning Good State and Action Representations via Tensor Decomposition","date":"2021-05-03","arxiv_id":"2105.01136","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-mirror-descent-for-low-rank-tensor","title":"Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses","date":"2021-04-29","arxiv_id":"2104.14562","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-dimensional-doa-estimation-for-l-shaped","title":"Two-Dimensional DOA Estimation for L-shaped Nested Array via Tensor Modeling","date":"2021-04-14","arxiv_id":"2104.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-extremely-compact-rnns-for-video","title":"Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure","date":"2021-04-12","arxiv_id":"2104.05758","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovery-of-joint-probability-distribution","title":"Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections","date":"2021-03-22","arxiv_id":"2103.11864","repositories_listed":0,"syntology":null},{"url":"/paper/spatial-temporal-tensor-graph-convolutional","slug":"spatial-temporal-tensor-graph-convolutional","title":"Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction","date":"2021-03-10","arxiv_id":"2103.06126","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetry-breaking-in-symmetric-tensor","title":"Symmetry Breaking in Symmetric Tensor Decomposition","date":"2021-03-10","arxiv_id":"2103.06234","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-slice-low-rank-tensor-decomposition","title":"Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation","date":"2021-02-24","arxiv_id":"2102.12056","repositories_listed":0,"syntology":null},{"url":null,"slug":"alma-alternating-minimization-algorithm-for","title":"ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network","date":"2021-02-20","arxiv_id":"2102.10226","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-misinformation-from-website","title":"Identifying Misinformation from Website Screenshots","date":"2021-02-15","arxiv_id":"2102.07849","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetric-boolean-factor-analysis-with","title":"Symmetric Sparse Boolean Matrix Factorization and Applications","date":"2021-02-02","arxiv_id":"2102.01570","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-overfitting-avoidance-tuning-free","title":"Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications","date":"2021-01-24","arxiv_id":"2101.09672","repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-based-discriminant-analysis-for","title":"Riemannian Manifold Optimization for Discriminant Subspace Learning","date":"2021-01-20","arxiv_id":"2101.08032","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperntf-a-hypergraph-regularized-nonnegative","title":"HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction","date":"2021-01-18","arxiv_id":"2101.06827","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-observed-causal-effects-from","title":"Disentangling Observed Causal Effects from Latent Confounders using Method of Moments","date":"2021-01-17","arxiv_id":"2101.06614","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-block-term-tensor","title":"Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way","date":"2021-01-08","arxiv_id":"2101.02931","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensorizing-subgraph-search-in-the-supernet","title":"Topology-aware Tensor Decomposition for Meta-graph Learning","date":"2021-01-04","arxiv_id":"2101.01078","repositories_listed":0,"syntology":null},{"url":null,"slug":"quaternion-higher-order-singular-value","title":"Quaternion higher-order singular value decomposition and its applications in color image processing","date":"2021-01-02","arxiv_id":"2101.00364","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-compressed-convolution-neural","title":"Rethinking Compressed Convolution Neural Network from a Statistical Perspective","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"alternating-linear-scheme-in-a-bayesian","title":"Alternating linear scheme in a Bayesian framework for low-rank tensor approximation","date":"2020-12-21","arxiv_id":"2012.11228","repositories_listed":0,"syntology":null}],"record_sha256":"994c9c614f363409d546175d7bf7f2d79cac6f87abbe6f357dfdf5cccc5cdcbc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}