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..
Unbiased Online Recurrent Optimization
Authors: Corentin Tallec, Yann Ollivier
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, UORO is shown to provide convergence on a set of synthetic experiments where truncated BPTT fails to display reliable convergence. An implementation of UORO is provided as supplementary material. |
| Researcher Affiliation | Academia | Corentin Tallec Laboratoire de Recherche en Informatique Université Paris Sud Gif-sur-Yvette, 91190, France EMAIL Yann Ollivier Laboratoire de Recherche en Informatique Université Paris Sud Gif-sur-Yvette, 91190, France EMAIL |
| Pseudocode | Yes | The resulting algorithm is detailed in Alg. 1. |
| Open Source Code | Yes | An implementation of UORO is provided as supplementary material. |
| Open Datasets | Yes | To monitor the variance of UORO s estimate over time, a 64-unit GRU recurrent network is trained on the first 107 characters of the full works of Shakespeare using UORO. |
| Dataset Splits | No | The paper describes training on sequences and evaluation, but does not specify a distinct validation set with explicit split percentages or counts for hyperparameter tuning. For example, "Optimization was performed using Adam with the default setting β1 = 0.9 and β2 = 0.999, and a decreasing learning rate ηt = γ 1+α t, with t the number of characters processed." |
| Hardware Specification | No | The paper mentions using a "64-unit GRU recurrent network" but does not specify any hardware components like CPU or GPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper mentions using "Adam with the default setting β1 = 0.9 and β2 = 0.999" and "vanilla SGD", but does not provide version numbers for any specific software libraries or frameworks (e.g., TensorFlow, PyTorch, Python version) that would be needed for replication. |
| Experiment Setup | Yes | Optimization was performed using Adam with the default setting β1 = 0.9 and β2 = 0.999, and a decreasing learning rate ηt = γ 1+α t, with t the number of characters processed. ... (with learning rates using α = 0.015 and γ = 10 3). ... The learning rates used α = 0.03 and γ = 10 3. |