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Tensors@IEEE-DSAA 2021 : Tensor Analytics for Emerging Applications Special Session @IEEE-DSAA21 | |||||||||||||||
Link: http://dsaa2021.tensors.eu.org/ | |||||||||||||||
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Call For Papers | |||||||||||||||
Call for Papers: Special Session TENSOR ANALYTICS FOR EMERGING APPLICATIONS @ IEEE DSAA 20216-9 OCTOBER 2021, ONLINE
http://dsaa2021.tensors.eu.org/ Tensor decompositions are the "Swiss knife" of data science, data mining, machine learning and signal processing. They can be used for a variety of problems such as factor analysis; co-clustering; outlier/anomaly detection; and missing value estimation and interpolation; link prediction in time-evolving networks; multivariate time series forecasting; feature extraction in classification, as well as compression/de-noising and pattern recognition in signal processing. The recent research shows that tensor-based methods beat almost all non-tensor-based methods in a wide range of real-world applications. If we want to name a single tool, equivalent to deep learning but in the unsupervised setting that tool is perhaps tensor decompositions. Even, recently it is shown that tensor decompositions have applications in compressing the parameter space of deep learning models. The main reason behind this versatility is that the natural structure of many real-world datasets is highly multi-way in many applications and tensor decompositions are capable of capturing those complex interactions across various ways of data and thus provide a better and more natural model. The Special session on "Tensor Analytics for Emerging Applications" aims at bringing together computer scientists, data scientists, and domain experts from various application areas to discuss the recent advances in algorithms, models, scalable solutions, and real-life applications. We expect diverse submissions spanning and integrating fields such as healthcare (e.g., analysis of COVID-19 data and phenotyping), industry (e.g., IoT and sensors), internet platforms (e.g., recommender systems and time-evolving social network analysis), transportation (e.g. interpolation of traffic data and analysis of spatio-temporal data), neuroscience (e.g., modelling EEG and fMRI imaging data), and remote sensing (e.g., modelling hyperspectral images). We invite submissions that report progress in either theoretical, technical or application aspects of Tensor Analytics. The topics include, but are not limited to the following: New Models for Tensor Decompositions New Fitting Algorithms for Tensor Decomposition Models New Fitting Algorithms for Constrained (e.g., non-negative) Tensor Decompositions New Models for Coupled Tensor/Matrix Decompositions New Efficient Algorithms for Sparse Tensors Bayesian/Probabilistic Models Tensor Network Distributed, Parallel and GPU-based solutions for tensor decompositions Sketching Solutions for Tensor Decompositions Incremental/Streaming/Multi-Aspect-Streaming Methods for Tensor Analysis Model and Model Order Selection for Tensors Time-aware Tensor Decompositions Space-time Aware Tensor Decompositions Simulation of Synthetic and Semi-realistic Tensor Data Benchmarking Studies Tensor Decompositions in Deep Learning Tensor-based Feature Extraction for Classification Tensor-based Anomaly Detection Tensor-based Recommender Systems Tensor-based Social Network Analysis Tensor-based Time Series Analysis and Forecasting Tensor-based Data Fusion Tensor-based Embeddings (e.g., RESCAL for knowledge graphs) Tensor Decompositions in Data Interpolation and Missing Value Estimation Applications in Emerging Healthcare Problems (e.g., COVID-19) Applications in Industry (e.g., IoT and Sensors) Applications in Medicine Applications in Neuroscience Applications in Remote sensing Applications in Transportation Applications in Biology Applications in Physics Real-life Case Studies Software Packages for Tensor Decompositions IMPORTANT DATES Paper Submission: May 23, 2021 Paper Notification: July 25, 2021 Paper Camera Ready Due: August 8, 2021 ORGANIZERS Evangelos E. Papalexakis,University of California Reiverside, USA Hadi Fanaee-T, Halmstad University, Sweden Program Committee Members Panos Markopoulos, Assistant Professor, Rochester Institute of Technology, USA Kijung Shin, Assistant Professor, KIST, South Korea Xiao Fu, Assistant Professor, Oregon State University, USA Shaden Smith, Senior Research SDE, Microsoft, USA Joyce C Ho, Assistant Professor, Emory University, USA Kejun Huang, Assistant Professor, University of Florida, USA Evangelos E. Papalexakis, Assistant Professor, University of California Riverside, USA Hadi Fanaee-T, Assistant Professor, Halmstad University, Sweden Maryam Amoozegar, Assistant Professor, Kerman Graduate University of Technology, Iran Sofia Fernandes, Postdoc, University of Aveiro, Portugal Mehran Yazdi, Professor, Shiraz University, Iran |
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