[02285] New Trends in Tensor Networks and Tensor Optimization
Session Date & Time :
02285 (1/2) : 4D (Aug.24, 15:30-17:10)
02285 (2/2) : 4E (Aug.24, 17:40-19:20)
Type : Proposal of Minisymposium
Abstract : Tensors have been shown to be a powerful tool for capturing multiple interactions and inherent hierarchies in data sets from wide applications in scientific and engineering communities. This minisymposium aims to bring together recent advances in tensor network analysis and large-scale tensor optimization. The topics of interest include, but are not limited to
– new advances in tensor networks for machine learning,
– tensorial time series analysis and deep learning,
– tensor regularized generalization in reinforcement learning,
– structural tensor analysis and applications,
– multilinear PageRank and data clustering.
Organizer(s) : Qibin Zhao, Yannan Chen, Andong Wang
Qibin Zhao (RIKEN Center for Advanced Intelligence Project)
Chunfeng Cui (Beihang University)
Chao Li (RIKEN Center for Advanced Intelligence Project)
Yuning Qiu (Guangdong Unviersity of Technology)
Weiyang Ding (Fudan University)
Jingya Chang (Guangdong University of Technology)
Mingyuan Bai (RIKEN Center for Advanced Intelligence Project)
Yannan Chen (South China Normal University)
Talks in Minisymposium :
[03043] Multilinear Pseudo-PageRank for Hypergraph Partitioning
Author(s) :
Yannan Chen (South China Normal University)
Abstract : In this talk, we establish the higher-order pseudo-PageRank model, which is formulated as a multilinear system with nonnegative constraints. The coefficient tensor of the multilinear system is a kind of Laplacian tensor of the uniform hypergraph and no dangling corrections are involved. Then, a tensor splitting algorithm is utilized for solving the higher-order pseudo-PageRank problem, of which solutions exist but may not be unique. Numerical experiments illustrate that the proposed higher-order pseudo-PageRank method is powerful and effective for hypergraph partitioning problems.
[03184] Tensor network strcuture search
Author(s) :
Chao Li (RIKEN)
Abstract : In this talk, we present a novel problem related to model selection for tensor networks, which we call tensor network structure search (TN-SS). TN-SS aims to find the optimal tensor network structure for a given dataset and task by exploring a large space of possible network structures. We propose several promising solutions to the TN-SS problem, including evolutionary algorithms, stochastic search, and alternating enumeration. Our methods are designed to efficiently explore the space of tensor network structures and identify the most promising candidates based on their performance on the given task.
[03267] Efficient Machine Learning with Tensor Networks
Author(s) :
Qibin Zhao (RIKEN AIP)
Abstract : Tensor Networks (TNs) are factorizations of high dimensional tensors into networks of many low-dimensional tensors, which have been studied in quantum physics, high-performance computing, and applied mathematics. In recent years, TNs have been increasingly investigated and applied to machine learning and signal processing, due to its significant advances in handling large-scale and high-dimensional problems, model compression in deep neural networks, and efficient computations for learning algorithms. This talk aims to present some recent progress of TNs technology applied to machine learning from perspectives of basic principle and algorithms, novel approaches in unsupervised learning, tensor completion, multi-model learning and various applications in DNN, CNN, RNN and etc.
[03838] A gradient projection method for semi-supervised hypergraph clustering problems
Author(s) :
Jingya Chang (Guangdong University of Technology)
Abstract : We use the hypergraph related tensor to construct an orthogonal constrained optimization model for the semi-supervised hypergraph problems, which is solved by a retraction method. A nonmonotone curvilinear search is implemented to guarantee reduction in the objective function value. Experiments on synthetic hypergraph and hypergraph given by real data demonstrate the effectivity of our method.
[04276] Tensorial Time Series Prediction via Tensor Neural Differential Equations
Author(s) :
Mingyuan Bai (RIKEN AIP)
Abstract : The recent decade has witnessed the surge of models and applications in multi-dimensional, i.e., tensorial time series analysis, where their entanglement of different aspects of data, i.e., modes, appeals to both academia and industry, and raises a number of challenges for modeling and analysis. To address these challenges, we aim to introduce tensor neural differential equations for tensorial time series analysis, including tensor neural ordinary differential equations and tensor neural controlled differential equations, etc.
[04779] Towards Multi-modes Outlier Robust Tensor Ring Decomposition
Author(s) :
Yuning Qiu (Guangdong University of Technolog)
Abstract : The outliers assumption in conventional robust tensor decomposition is often not true in tensors since high-order tensors are prone to be corrupted by outliers in more than one direction. To mitigate this weakness, we propose a novel outlier robust tensor decomposition (ORTD) model by capturing low-rank tensors corrupted from multi-mode outliers. To theoretically guarantee statistical performance, we rigorously analyze a non-asymptotic upper bound of the estimation error for the proposed ORTD model.
