330 citations · 358 across the 13 of their papers we have counts for
4 papers · 1 filter
Black box approximation in the tensor train format initialized by ANOVA decomposition
Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets
Surrogate models can reduce computational costs for multivariable functions with an unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is equiv…
Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI
Semen Budennyy, Vladimir Lazarev, Nikita Zakharenko +9
The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce…
T4DT: Tensorizing Time for Learning Temporal 3D Visual Data
Mikhail Usvyatsov, Rafael Ballester-Rippoll, Lina Bashaeva +3
Unlike 2D raster images, there is no single dominant representation for 3D visual data processing. Different formats like point clouds, meshes, or implicit functions each have thei…
Tensor networks in machine learning
Richik Sengupta, Soumik Adhikary, Ivan Oseledets +1
A tensor network is a type of decomposition used to express and approximate large arrays of data. A given data-set, quantum state or higher dimensional multi-linear map is factored…