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..
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Authors: Ahmed Alaa, Mihaela Van Der Schaar
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using data from a critical care setting, we demonstrate the utility of uncertainty quantification in sequential decision-making. |
| Researcher Affiliation | Academia | 1University of California, Los Angeles 2Cambridge University. |
| Pseudocode | Yes | Algorithm 1 BJ-based uncertainty estimation in RNNs |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code. |
| Open Datasets | Yes | We conducted experiments on data from the Medical Information Mart for Intensive Care (MIMIC-III) (Johnson et al., 2016) database |
| Dataset Splits | No | We generated 1000 training sequences using the model in (9) with T = 10 time steps and trained a vanilla RNN model to predict the label yt on the basis of the input sequence x1:t. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper discusses various RNN architectures (simple RNN, LSTMs, GRUs) and models but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | To ensure a fair comparison, we fix the hyperparameters of the underlying RNNs in all baselines to the following: Number of layers: 1, Number of hidden units: 20, Learning rate: 0.01, Batch size: 150, Number of epochs: 10, Number of steps: 1000. |