Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Gradient-based Hyperparameter Optimization through Reversible Learning
Authors: Dougal Maclaurin, David Duvenaud, Ryan Adams
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,894 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,595 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section shows several proof-of-concept experiments in which we can more richly parameterize training and regularization schemes in ways that would have been previously impractical to optimize. The network was trained on 10,000 examples of MNIST |
| Researcher Affiliation | Academia | Dougal Maclaurin EMAIL David Duvenaud EMAIL Ryan P. Adams EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic gradient descent with momentum |
| Open Source Code | Yes | Since we required access to the internal logic of RMD in order to implement Algorithm 2, we implemented our own automatic differentiation package for Python, available at github.com/HIPS/autograd. |
| Open Datasets | Yes | The network was trained on 10,000 examples of MNIST |
| Dataset Splits | Yes | Figure 7 shows a training set, the pixels of which were optimized to improve performance on a validation set of 10,000 examples from MNIST. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components (e.g., GPU models, CPU types, or cloud instance specifications) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Python' and 'Numpy (Oliphant, 2007)' as software used for their automatic differentiation package, but it does not specify any version numbers for these software dependencies or any other libraries. |
| Experiment Setup | Yes | Each meta-iteration trained a network for 100 iterations of SGD, meaning that the learning rate schedules were specified by 800 hyperparameters (100 iterations 4 layers 2 types of parameters)... The network was trained on 10,000 examples of MNIST, and had 4 layers, of sizes 784, 50, 50, and 50... We typically ran for 50 meta-iterations, and used a meta-step size of 0.04. |