Concept
Tensor decomposition
Publications associées (22)
COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION
Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine ...
Philadelphia2024WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures
With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high qual ...
MDPI2022Leveraging topology, geometry, and symmetries for efficient Machine Learning
When learning from data, leveraging the symmetries of the domain the data lies on is a principled way to combat the curse of dimensionality: it constrains the set of functions to learn from. It is more data efficient than augmentation and gives a generaliz ...
EPFL2022Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks
Sylvain Calinon, Suhan Narayana Shetty
In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring t ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques
Francisco Santos Paredes Quartin de Macedo
Stochastic models for interacting processes feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the numerical analysis of such Markov chains. In this wo ...
Springer Int Publishing Ag2016Riemannian Optimization For High-Dimensional Tensor Completion
Michael Maximilian Steinlechner
Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem rest ...
Siam Publications2016