Generalized Tensor Models for Recurrent Neural Networks
Authors: Valentin Khrulkov, Oleksii Hrinchuk, Ivan Oseledets
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are verified by a series of extensive computational experiments. In this section, we study if our theoretical findings are supported by experimental data. In particular, we investigate whether generalized tensor networks can be used in practical settings, especially in problems typically solved by RNNs (such as natural language processing problems). |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Moscow Institute of Physics and Technology, Moscow, Russia 3Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | For the first experiment, we use two computer vision datasets MNIST (Le Cun et al., 1990) and CIFAR 10 (Krizhevsky & Hinton, 2009), and natural language processing dataset for sentiment analysis IMDB (Maas et al., 2011). |
| Dataset Splits | No | The paper mentions training and testing but does not specify validation splits or proportions. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | Parameters of all networks were optimized using Adam (learning rate α = 10 4) and batch size 250. |