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..
Directed Chain Generative Adversarial Networks
Authors: Ming Min, Ruimeng Hu, Tomoyuki Ichiba
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed DC-GANs are examined on four datasets, including two stochastic models from social sciences and computational neuroscience, and two real-world datasets on stock prices and energy consumption. |
| Researcher Affiliation | Academia | 1Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106-3110, USA. 2Department of Mathematics, University of California, Santa Barbara, CA 93106-3080, USA. |
| Pseudocode | Yes | Algorithm 1 Generator in the Decorrelating and Branching |
| Open Source Code | Yes | The example implementation of DCGAN is available at https://github.com/mmin0/Directed_Chain_SDE. |
| Open Datasets | Yes | The third real-world data set of stock price time series was extracted from Yahoo Finance1, and the fourth real-world energy consumption data were obtained from Ireland s open data portal2. 1https://finance.yahoo.com/quote/GOOG?p=GOOG&.tsrc=fin-srch. 2https://data.gov.ie/dataset?theme=Energy. |
| Dataset Splits | No | We first generate the same amount of fake data paths as true data paths to avoid imbalance, and choose 80% from both real and fake data as training data, leaving the rest 20% as testing data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as exact GPU or CPU models. |
| Software Dependencies | No | We remark that DC-GANs can be adapted to torchsde3 framework and use their adjoint method for back-propagation. 3See the Python package https://github.com/google-research/torchsde. |
| Experiment Setup | Yes | We use a two-layer LSTM classifier with channels/2 as the size of the hidden state, where channels is the dimension of generated and real series. We will minimize the cross-entropy loss, and the optimization is done by Adam optimizer with a learning rate of 0.001 for 5000 iterations. |