Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exploring interpretable LSTM neural networks over multi-variable data
Authors: Tian Guo, Tao Lin, Nino Antulov-Fantulin
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. |
| Researcher Affiliation | Academia | 1ETH, Zürich, Switzerland 2EPFL, Switzerland. Correspondence to: Tian Guo <EMAIL>. |
| Pseudocode | No | The paper does not contain any sections explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there any structured code-like blocks detailing a procedure. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | Yes | PLANT: This records the time series of energy production of a photo-voltaic power plant in Italy (Ceci et al., 2017).; SML is a public dataset used for indoor temperature forecasting. Same as (Qin et al., 2017), |
| Dataset Splits | Yes | PLANT: It provides 20842 sequences split into training (70%), validation (10%) and testing sets (20%).; SML: The first 3200, the following 400 and the last 537 data points are respectively used for training, validation, and test. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper states 'We implemented IMV-LSTM and deep learning baselines with Tensorflow.' However, it does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | We used Adam with the mini-batch size 64 (Kingma & Ba, 2014). For the size of recurrent and dense layers in the baselines, we conduct grid search over {16, 32, 64, 128, 256, 512}. The size of IMV-LSTM layers is set by the number of neurons per variable selected from {10, 15, 20, 25}. Dropout is selected in {0, 0.2, 0.5}. Learning rate is searched in {0.0005, 0.001, 0.005, 0.01, 0.05}. L2 regularization is added with the coefficient chosen from {0.0001, 0.001, 0.01, 0.1, 1.0}. |