Input-Output Equivalence of Unitary and Contractive RNNs
Authors: Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical results are supported by experiments on modeling of slowly-varying dynamical systems. |
| Researcher Affiliation | Academia | Melikasadat Emami Dept. ECE UCLA emami@ucla.edu; Mojtaba Sahraee-Ardakan Dept. ECE UCLA msahraee@ucla.edu; Sundeep Rangan Dept. ECE NYU srangan@nyu.edu; Alyson K. Fletcher Dept. Statistics UCLA akfletcher@ucla.edu |
| Pseudocode | No | The paper does not include a dedicated section or figure labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes how they generated their own synthetic dataset ('we generate data from multiple instances of a synthetic RNN'), rather than using an existing publicly available or open dataset with access information. No link or citation to a public dataset is provided. |
| Dataset Splits | No | The paper mentions generating '700 training samples and 300 test sequences' but does not specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as CPU/GPU models, memory, or cloud computing instance types. |
| Software Dependencies | No | The paper states 'All models are implemented in the Keras package in Tensorflow.' However, it does not specify version numbers for Keras or TensorFlow. |
| Experiment Setup | Yes | The hidden states in the model are varied in the range n = [2, 4, 6, 8, 10, 12, 14]... We used mean-squared error as the loss function. Optimization is performed using Adam [15] optimization with a batch size = 10 and learning rate = 0.01. |