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..
Fluctuation-dissipation relations for stochastic gradient descent
Authors: Sho Yaida
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our claims are empirically verified. |
| Researcher Affiliation | Industry | Sho Yaida Facebook AI Research Facebook Inc. Menlo Park, California 94025, USA EMAIL |
| Pseudocode | No | The paper describes algorithms and equations but does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository was found. |
| Open Datasets | Yes | a multilayer perceptron (MLP) learning patterns in the MNIST training data (Le Cun et al., 1998) through SGD without momentum and a convolutional neural network (CNN) learning patterns in the CIFAR10 training data (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper mentions training and test data but does not explicitly describe a validation dataset split or a methodology for it. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processors, or memory specifications used for running experiments were provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | For both models, the mini-batch size is set to be |B| = 100, and the training data are shuffled at each epoch... the L2-regularization term 1/2λθ^2 with the weight decay λ = 0.01 is included in the loss function f. The MLP is initialized through the Xavier method (Glorot & Bengio, 2010) and trained for ˆttotal epoch = 100 epochs with the learning rate η = 0.1. |