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..
Learning by Turning: Neural Architecture Aware Optimisation
Authors: Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents experiments intended to demonstrate Nero s key properties. In all figures, the mean and range are plotted over three repeats. |
| Researcher Affiliation | Collaboration | 1Abacus.AI 2Caltech. |
| Pseudocode | Yes | Algorithm 1 Nero optimiser. Out-of-the-box hyperparameter defaults are 0.01 and β 0.999. The constant σb P R refers to the initialisation scale of the biases. |
| Open Source Code | Yes | Code available at github.com/jxbz/nero. |
| Open Datasets | Yes | A VGG-11 image classifier on the CIFAR-10 dataset, [...] classify the MNIST dataset. [...] train a language model on the Wikitext-2 dataset, and a larger transformer (121 tensors) trained on WMT2016 English German translation. [...] PPO on the Atari Pong video game. [...] Res Net-50 classifier on the Image Net dataset. |
| Dataset Splits | No | The paper mentions using well-known datasets like CIFAR-10, MNIST, Wikitext-2, and ImageNet, which typically have predefined splits. It also refers to 'validation error' and 'validation results'. However, it does not explicitly state the specific percentages or sample counts for train/validation/test splits used in its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'Pytorch implementation' but does not specify version numbers for PyTorch or any other software dependencies, making it difficult to precisely reproduce the environment. |
| Experiment Setup | Yes | For Nero, out-of-the-box refers to setting 0.01 and β 0.999. [...] Learning rates were tuned over t10 4, 10 3, ..., 100u. [...] β in Nero and β2 in Adam and LAMB were fixed to 0.999 across all experiments. [...] Typical initialisation scales are σb 1 for gains and σb 0.01 for biases. |