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..
A Free-Energy Principle for Representation Learning
Authors: Yansong Gao, Pratik Chaudhari
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental validation of the theoretical results is provided on standard imageclassification datasets. |
| Researcher Affiliation | Academia | 1University of Pennsylvania, USA. Correspondence to: Yansong Gao <EMAIL>, Pratik Chaudhari <EMAIL>. |
| Pseudocode | No | The paper describes algorithms and processes textually and through equations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We use the MNIST (Le Cun et al., 1998) and CIFAR-10 (Krizhevsky, 2009) datasets for our experiments. |
| Dataset Splits | No | The paper mentions 'validation loss' and 'validation accuracy' in figures and text (e.g., Figures 2 and 3), implying the use of a validation set, but it does not specify the dataset splits (e.g., percentages or exact counts for train/validation/test) in the main text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like Adam, but it does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Given a value of the Lagrange multipliers (λ, γ) we first find a model on the equilibrium surface by training from scratch for 120 epochs with the Adam optimizer (Kingma & Ba, 2014); the learning rate is set to 10 3 and drops by a factor of 10 every 50 epochs. The maximum value of the learning rate is set to 1.5 10 3. We train on source dataset for 300 epochs with Adam and a learning rate of 1E-3 that drops by a factor of 10 after every 120 epochs. |