Information on published tensor decomposition algorithms. by Jiaqi Liu (309587)

“…<p>Information on published tensor decomposition algorithms.</p>…”

Bayesian Inference of Vector Autoregressions with Tensor Decompositions by Yiyong Luo (6170300)

“…However, VARs can be over-parameterized if the numbers of variables and lags are moderately large. Tensor VAR, a recent solution to over-parameterization, treats the coefficient matrix as a third-order tensor and estimates the corresponding tensor decomposition to achieve parsimony. …”

Equilibrium convergence performance of strategy decomposition algorithm. by Dandan Wang (286632)

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Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data by Jiuyun Hu (13792760)

“…<p>We propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. …”

Conditional probability tensor decompositions for multivariate categorical response regression by Aaron J. Molstad (5243561)

“…Our method relies on a functional probability tensor decomposition: a decomposition of a tensor-valued function such that its range is a restricted set of low-rank probability tensors. …”

CP decomposition Bayes network. by Jiaqi Liu (309587)

“…CANDECOMP/PARAFAC (CP) is a popular tensor decomposition model, which is both theoretically advantageous and numerically stable. …”

(a) Prediction using traditional algorithm. (b) Prediction using optimization algorithm. by Dandan Wang (286632)

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Steps of the Logistics Capacity Game Algorithm. by Dandan Wang (286632)

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Performance comparison between meta-gradient driven and benchmark algorithms. by Dandan Wang (286632)

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(a) Traditional economic benefits. (b) Economic benefits after algorithm optimization. by Dandan Wang (286632)

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The structure diagram of the BS-CP algorithm. by Jiaqi Liu (309587)

“…CANDECOMP/PARAFAC (CP) is a popular tensor decomposition model, which is both theoretically advantageous and numerically stable. …”

Efficient Estimation for Longitudinal Networks via Adaptive Merging by Haoran Zhang (731854)

“…In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. …”

Grid search results for p value on Salinas-simulated dataset. by Ruhan A (12403982)

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Grid search results of dual-window parameters on AVIRIS-I dataset. by Ruhan A (12403982)

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Color detection maps for abu-urban-3 dataset. by Ruhan A (12403982)

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(a) AUC values and (b) ROC curves for the abu-urban-3 dataset. by Ruhan A (12403982)

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Grid search results of dual-window parameters on Salinas-simulated dataset. by Ruhan A (12403982)

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Color detection maps for the AVIRIS-II dataset. by Ruhan A (12403982)

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AUC value comparison. by Ruhan A (12403982)

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(a) AUC values and (b) ROC curves for the Salinas-simulated dataset. by Ruhan A (12403982)

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