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..
Feature Learning and Signal Propagation in Deep Neural Networks
Authors: Yizhang Lou, Chris E Mingard, Soufiane Hayou
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate an excellent match with the theoretical predictions. |
| Researcher Affiliation | Academia | 1St John s College, University of Oxford, Oxford, UK 2PTCL, University of Oxford, Oxford, UK 3Department of Physics, University of Oxford, UK 4Department of Mathematics, National University of Singapore. |
| Pseudocode | Yes | Algorithm 1 Layer-wise maximisation of features |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | Example Alignment Hierarchies for 20 layer FFNNs trained on Fashion MNIST (L) and CIFAR10 (R). ... fully-connected networks trained on an FFNN with depth 10 and width 256. ...trained on the MNIST/Fashion MNIST/CIFAR10 datasets. |
| Dataset Splits | Yes | training and validation set split of 45000/5000 |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models) used for its experiments. |
| Software Dependencies | No | The paper mentions optimizers like SGD, but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | optimised with SGD with weight decay, momentum, and learning rates of 0.003. ... See Fig. 8 for clear demonstration of the change in C with learning rate. ... Tables 1, 2, and 3 detail depth, width, learning rate, and epochs used. |