Campus health and safety are our top priorities. An introduction to tensors for students of physics and engineering. 2012. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech … Sidiropoulos, Nicholas D., et al. Canonical Polyadic Tensor Decomposition Identifies Inputs to Artificial Networks Alex Williams* Deep Learning in Multi-Layer Architectures of Dense Nuclei Yonghua Yin*, Erol Gelenbe ; CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling IEEE Transactions on Signal Processing 65.13 (2017): 3551-3582. Continuous Low-Rank Tensor Decompositions, With Applications to Stochastic Optimal Control and Data Assimilation Alex Arkady Gorodetsky, Computational Science & Engineering (Aeronautics & Astronautics) Advisors: Sertac Karaman and Youssef M. Marzouk. Google Scholar; Joseph C Kolecki. (2019) Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction. SIAM Rev. T. Kolda and B. Bader. Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda; Interval timing in deep reinforcement learning agents Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo However, we currently lack a theoretical understanding of the algorithmic behavior of low-rank tensor decompositions. Workshop track - ICLR 2018 LEARNING DYNAMICS OF DEEP NETWORKS ADMIT LOW-RANK TENSOR DESCRIPTIONS Christopher H. Stock 1, Alex H. Williams , Madhu S. Advani2, Andrew M. Saxe2, Surya Ganguli1 1Stanford University, Stanford, CA, USA; 2Harvard University, Cambridge, MA, USA fchstock,ahwillia,sgangulig@stanford.edu, fmadvani,asaxeg@fas.harvard.edu ABSTRACT Deep … "Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis." Christopher H. Stock, Alex H. Williams, Madhu S. Advani, Andrew M. Saxe, Surya Ganguli 12 Feb 2018 (modified: 04 Jun 2018) ICLR 2018 Workshop Submission Readers: Everyone Keywords : Learning Dynamics, Deep Networks, Tensor Decomposition This software was written and packaged by Alex H. Williams (ahwillia @ stanford. ... 11/9/2016 Kolda on Tensor Decompositions @ MIDAS 18 # variables = R(I + J + K) # data points = IJK Rank = minimal R to exactly reproduce tensor . (2002). 2009. In this article, we introduce a deeply tensor-compressed video comprehension neural network, called DEEPEYE, for inference on terminal devices. Sandia applies cutting-edge technologies in integrated … Tensor Decompositions are the New Matrix Decompositions 2/2/2017 Kolda - IPAM Big Data Meets Computation 5 Singular value decomposition (SVD), eigendecomposition (EVD), nonnegative matrix factorization (NMF), sparse SVD, etc. Stanford University. Objects that tensors may map between include, but are not limited to vectors and scalars, and, recursively, even other tensors (for example, a matrix is a map between vectors, and is thus a tensor.Therefore a linear map between matrices is also a tensor). Viewpoint 1: Sum of outer products, useful for interpretation Viewpoint 2: High-variance subspaces, useful for compression for the US Department of Energy's National Nuclear Security Administration. Get the latest from UT on COVID-19. Sandia is a multiprogram engineering and science laboratory operated by National Technology and Engineering Solutions of Sandia, LLC. Such decompositions are known by the name of rank-decomposition, CANDECOMP/PARAFAC or CP decomposition (see kolda2009tensor for an extensive review). Tensor Decomposition: A Mathematical Tool for Data Analysis and Compression Abstract ... (Sandia), Hemanth Kolla (Sandia), and Alex Williams (Stanford University). Alex H Williams. In the next release, we hope to implement randomized algorithms, basic regularization, and cross-validation. ... low-dimensional neural dynamics across multiple timescales through tensor component analysis. Borbála Hunyadi. Combining tensor decomposition and time warping models for multi-neuronal spike train analysis. Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data. A tensor T is supersymmetric if there exists a decomposition of the form T = r ∑ i = 1 ω ( q ) i . Volume Edited by: Kamalika Chaudhuri Masashi Sugiyama Series Editors: Neil D. Lawrence Mark Reid Low-rank tensor decomposition then arises as a powerful and widely used tool to discover simple low dimensional structures underlying such data. Tensor Wikipedia In mathematics, a tensor is an algebraic object that describes a linear mapping from one set of algebraic objects to another. Instead of building a Long Short-Term Memory (LSTM) network directly from high-dimensional raw video data input, we construct an LSTM-based spatio-temporal model from structured, tensorized time-series features for object detection and … A. Williams, H. Kim, F. Wang, S. Vyas, K. Shenoy, M. Schnitzer and T. K. andSurya Ganguli, Dimension Reduction of Multi-trial Neural Data by Tensor Decomposition (extended abstract), Computational and Systems Neuroscience (Cosyne) 2017 (2017-02-23 to 2017-02-26), 2017 Accepted talk, with acceptance rate of 5% BibTeX: Continuous Low-Rank Tensor Decompositions, With Applications to Stochastic Optimal Control and Data Assimilation. Basic algorithms and functionality for CP tensor decomposition are available and stable. Mikl os P al a, Sungkyunkwan University and MTA-DE \Lendulet" Functional Analysis Research Group, Taussky-Todd Lecturer, Tuesday, 8:50, Hoover 2055 Illustration of tensor unfolding for applying matrix decompositions to ... and a \trials unfolding" with dimensions K NT. Alex Arkady Gorodetsky, Computational Science & Engineering (Aeronautics & Astronautics) Advisors: Sertac Karaman and Youssef M. Marzouk ∙ 0 ∙ share . Simulating Fluid-Solid Interaction Using Smoothed Particle Hydrodynamics Method 10/29/2019 ∙ by Maksim Kuznetsov, et al. Several studies have applied tensor decompositions to EEG and fMRI data, most typically to model differences across subjects or Fourier/wavelet transformed signals (Mørup et al., 2006, Acar et al., 2007, Cong et al., 2015, Hunyadi et al., 2017), rather than across trials (Andersen and Rayens, 2004, Mishne et al., 2016). This tutorial covers matrix and tensor factorizations - a large class of dimensionality-reduction methods that includes PCA, non-negative matrix factorization (NMF), independent components analysis (ICA), and others. A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. (A) The observation tensor Y is a sum of r rank − 1 tensors and a noise tensor with variance . "Tensor decomposition for signal processing and machine learning." Google Scholar; Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. describe an unsupervised method to uncover simple structure in large-scale recordings by extracting distinct cell assemblies with rapid within-trial dynamics, reflecting interpretable aspects of perceptions, actions, and thoughts, and slower across-trial … Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Michael Collins, Shay Cohen Action-Model Based Multi-agent Plan Recognition Hankz Zhuo, Qiang Yang, Subbarao Kambhampati Semiparametric Principal Component Analysis Fang Han, Han Liu Williams, Alex H., et al. Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Laboratories at MLconf SF 2017 1. Alex Williams - Ktensor/viz method (ktensor/viz) Shaden Smith - Faster fitness computation in cp_als . 2. Tensor Decompositions and Applications. Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. (B) Factor graph for the decomposition of an order-3 tensor. Sandia's enduring mission is to provide engineering and science support for America's nuclear weapons stockpile. Williams et al. Alex Williams (Stanford*) Plus many more former students and colleagues, collaborators for workshops, tutorials, etc. Verified email at stanford.edu - Homepage. CP Tensor Decomposition Casey Battaglino1, Grey Ballard2, and Tamara G. Kolda3 1cbattaglino3@gatech.edu 2ballard@wfu.edu 3tgkolda@sandia.gov Georgia Tech ... Alex Williams, Stanford/Sandia) PCA CP (2D) (3D) A Practical Randomized CP Tensor Decomposition SIAM CSE 2017. Alex H. Williams's 13 research works with 90 citations and 384 reads, including: Point process models for sequence detection in high-dimensional neural spike trains