15,213 Hits in 3.6 sec

Learning tensors from partial binary measurements [article]

Navid Ghadermarzy and Yaniv Plan and Ozgur Yilmaz
2018 pre-print
., when d=2, the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is the same as recovering it from unquantized measurements.  ...  ×R^N can be estimated efficiently by only m=O(Nd) binary measurements by regularizing its max-qnorm and M-norm as surrogates for its rank.  ...  Here the problem is recovering a low-rank matrix from binary (1-bit) measurements of a subset of its entries.  ... 
doi:10.1109/tsp.2018.2879031 arXiv:1804.00108v1 fatcat:o57no2gpfzgl7meizspglvonaa

E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients [article]

Anil R. Yelundur, Srinivasan H. Sengamedu, Bamdev Mishra
2018 arXiv   pre-print
And that the partial natural gradient learning outperforms stochastic gradient learning and Online-EM with sufficient statistics.  ...  Finally, we show that the P\'olya-Gamma formulation simplifies calculation of the Fisher information matrix for partial natural gradient learning.  ...  as compared with the AUC curve from stochastic gradient learning. 5.  ... 
arXiv:1804.03836v3 fatcat:mz6akwan6fcexn4w4otbhq2ucq

Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss [article]

Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
2013 arXiv   pre-print
We propose a modular framework for multi-relational learning via tensor decomposition.  ...  We learn this latent representation by computing a low-rank tensor decomposition, using quasi-Newton optimization of a weighted objective function.  ...  Acknowledgements This work was partially supported by NSF CAREER grant 0746930 and NSF grant IIS1218488.  ... 
arXiv:1303.1733v2 fatcat:2hvv6pp4tvhs7hdxqgczc5hjd4

Searching to Sparsify Tensor Decomposition for N-ary Relational Data [article]

Shimin Di, Quanming Yao, Lei Chen
2021 arXiv   pre-print
Specifically, we propose a new tensor decomposition framework, which allows embedding sharing to learn from facts with mixed arity.  ...  First, they suffer from the data-sparsity issue since they can only learn from the N-ary relational data with a specific arity, i.e., parts of common N-ary relational data.  ...  To handle the data sparsity issue, we propose to partially share embeddings across arities and jointly learn embeddings from the N-ary relational data with mixed arity.  ... 
arXiv:2104.10625v1 fatcat:z5piqqjudrda7hmrppq4j5u7rq

Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

Zhen Wang, Nannan Wu, Xiaohan Yang, Bingqi Yan, Pingping Liu
2021 Remote Sensing  
cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code.  ...  In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the  ...  To balance the training complexity and ANN search performance, PRH [23] employs the partial randomness and partial learning strategy to generate hashing functions.  ... 
doi:10.3390/rs13234786 fatcat:v6sf2zuk4vaezisevwbhpxpipq

A review of heterogeneous data mining for brain disorder identification

Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 Brain Informatics  
Other clinical measures are usually available reflecting the disease status from different perspectives.  ...  Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions.  ...  Subspace learning algorithms learn a latent subspace, from which multiple views are generated.  ... 
doi:10.1007/s40708-015-0021-3 pmid:27747561 pmcid:PMC4883173 fatcat:rhvqh4vmeffnnoxts7esxwxlsq

A study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognition

Alberto Garcia-Garcia, Jose Garcia-Rodriguez, Sergio Orts-Escolano, Sergiu Oprea, Francisco Gomez-Donoso, Miguel Cazorla
2017 Computer Vision and Image Understanding  
Those representations consist of a grid-like structure (fixed and adaptive) and a measure for the occupancy of each cell of the grid (binary and normalized point density).  ...  The network was imple- Figure 7 : 7 Binary tensor computed over a point cloud of a partial view of an object (shown inFigure 6).  ...  Second, we developed two possible occupancy measures for the volumetric elements of the tensor.  ... 
doi:10.1016/j.cviu.2017.06.006 fatcat:7bksmvldafbubh743cf5mflugu

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease

Joseph Giorgio, Susan Landau, William Jagust, Peter Tino, Zoe Kourtzi
2020 NeuroImage: Clinical  
We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable  ...  Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease  ...  ADNI is funded by the National Institute on aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following:  ... 
doi:10.1016/j.nicl.2020.102199 pmid:32106025 pmcid:PMC7044529 fatcat:ook3fyrikbchpcehtxrof7phvi

Multi-class quantum classifiers with tensor network circuits for quantum phase recognition [article]

Marco Lazzarin, Davide Emilio Galli, Enrico Prati
2021 arXiv   pre-print
Recently, tensor network-inspired circuits have been proposed as a natural choice for such ansatz. Their employment on binary classification tasks provided encouraging results.  ...  Hybrid quantum-classical algorithms based on variational circuits are a promising approach to quantum machine learning problems for near-term devices, but the selection of the variational ansatz is an  ...  The former retrieves the prediction from measurements in the computational basis.  ... 
arXiv:2110.08386v1 fatcat:aml5snnmljai3hh7fcle4xeq5q

An Advance on Variable Elimination with Applications to Tensor-Based Computation [article]

Adnan Darwiche
2020 arXiv   pre-print
We illustrate the efficacy of our proposed algorithm by compiling Bayesian network queries into tensor graphs and then learning their parameters from labeled data using a standard tool for tensor computation  ...  learning model parameters.  ...  This work has been partially supported by grants from NSF IIS-1910317, ONR N00014-18-1-2561 and DARPA N66001-17-2-4032.  ... 
arXiv:2002.09320v1 fatcat:etjp4ubtirfsfo3rjgtuhdhwba

Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge [article]

Luciano Serafini, Artur d'Avila Garcez
2016 arXiv   pre-print
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.  ...  Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning.  ...  We showed how real logic can be implemented in deep tensor networks, which we call Logic Tensor Networks (LTNs), and applied efficiently to knowledge completion and data prediction tasks.  ... 
arXiv:1606.04422v2 fatcat:jqttytjqefbdnlktw4pgjkzce4

Large-scale factorization of type-constrained multi-relational data

Denis Krompass, Maximilian Nickel, Volker Tresp
2014 2014 International Conference on Data Science and Advanced Analytics (DSAA)  
Relational type-constraints explicitly define the logic of relations by excluding entities from the subject or object role.  ...  In this paper we extend the recently proposed state-of-the-art RESCAL tensor factorization to consider relational type-constraints.  ...  The performance of the RESCAL tensor factorization on the partial observed tensor constructed from the various datasets as illustrated in Figure 2 .a.  ... 
doi:10.1109/dsaa.2014.7058046 dblp:conf/dsaa/KrompassNT14 fatcat:vq3fhf4dsnbohheksmymlq4f6a

Neural Game Engine: Accurate learning of generalizable forward models from pixels [article]

Chris Bamford, Simon Lucas
2020 arXiv   pre-print
Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels.  ...  Learning forward models is an interesting and important challenge in order to address problems where a model is not available.  ...  Reward accuracy is measured using precision, recall and f1 F r score of the binary classifications. D.  ... 
arXiv:2003.10520v2 fatcat:jwkuve6jefdnlpjntncd27ivqe

A Neural Network for Semigroups [article]

Edouard Balzin, Boris Shminke
2021 arXiv   pre-print
Tasks like image reconstruction in computer vision, matrix completion in recommender systems and link prediction in graph theory, are well studied in machine learning literature.  ...  In this work, we apply a denoising autoencoder-based neural network architecture to the task of completing partial multiplication (Cayley) tables of finite semigroups.  ...  The output tensor, the value of the autoencoder on such a partially filled table, would have to correspond to a) a table that b) is associative.  ... 
arXiv:2103.07388v1 fatcat:detdimtvffcxjonisuomx6ia5u

Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data [article]

Nathaniel Braman, Jacob W. H. Gordon, Emery T. Goossens, Caleb Willis, Martin C. Stumpe, Jagadish Venkataraman
2021 arXiv   pre-print
Prognostic embeddings from each modality are learned and combined via attention-gated tensor fusion.  ...  The model learns to combine information from multiparametric MRI exams, biopsy-based modalities (such as H&E slide images and/or DNA sequencing), and clinical variables into a comprehensive multimodal  ...  Binary low/high-risk groups were derived from the risk scores, where a risk score >0 corresponded to high risk.  ... 
arXiv:2107.00648v1 fatcat:qkd6vksmd5aq5jotznkukeu5kq
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