A Free-Energy Principle for Representation Learning
Authors: Yansong Gao, Pratik Chaudhari
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <gaoyans@sas.upenn.edu>, Pratik Chaudhari <pratikac@seas.upenn.edu>. |
| 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. |