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..
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Authors: David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a range of optimizers, layers, and tasks provide evidence that the analysis accurately captures the dynamics of neural optimization. |
| Researcher Affiliation | Collaboration | 1Victoria University of Wellington, New Zealand 2Disney Research, Z urich, Switzerland. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | No concrete access to source code (e.g., a specific repository link or explicit statement of code release in supplementary materials) is provided. |
| Open Datasets | Yes | Autoencoder trained on MNIST. Convnet trained on CIFAR-10. |
| Dataset Splits | No | No specific dataset split information (e.g., percentages, sample counts, or explicit mention of validation splits) is provided for reproducibility. |
| Hardware Specification | Yes | Some experiments were performed using a Tesla K80 kindly donated by Nvidia. |
| Software Dependencies | No | The paper mentions TensorFlow but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Autoencoder trained on MNIST. Dense layers with architecture 784 ! 50 ! 30 ! 20 ! 30 ! 50 ! 784 and Re LU non-linearities. Trained with MSE loss using minibatches of 64. Convnet trained on CIFAR-10. Three convolutional layers with stack size 64 and 5 5 receptive ๏ฌelds, Re LU nonlinearities and 2 2 max-pooling. Followed by a 192 unit fully-connected layer with Re LU before a ten-dimensional fully-connected output layer. Trained with cross-entropy loss using minibatches of 128. Learning rates were tuned for optimal performance. Additional parameters for Adam and RMSProp were left at default. |