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..
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Authors: Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an empirical analysis of the learning dynamics on easily analysable artificial data. We create 2 and 100 dimensional versions of four basic datasets (details in the Supplementary Materials Section D) and train four simple models (a linear model and a deep linear one with 10 hidden layers, trained to minimise MSE and log loss) with regular backprop and with a SG-based alternative to see whether it (numerically) converges to the same solution. |
| Researcher Affiliation | Industry | 1Deep Mind, London, United Kingdom. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor does it present structured steps formatted like code. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | We train deep relu networks of varied depth (up to 50 hidden layers) with batch-normalisation and with two different activation functions on MNIST and compare models trained with full backpropagation to variants that employ a SG module in the middle of the hidden stack. |
| Dataset Splits | No | The paper mentions experiments on MNIST, but it does not provide specific details on dataset splits (e.g., percentages, sample counts, or citations to standard splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments, only general statements about 'training neural networks'. |
| Software Dependencies | No | The paper does not provide specific details on software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions) used for the experiments. |
| Experiment Setup | Yes | We train deep relu networks of varied depth (up to 50 hidden layers) with batch-normalisation and with two different activation functions on MNIST and compare models trained with full backpropagation to variants that employ a SG module in the middle of the hidden stack. ... We train with a small L2 penalty added to weights... |