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 [1].
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data
Authors: Sung Woo Park, Kyungjae Lee, Junseok Kwon
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS", "Table 1: Evaluation of reconstruction tasks on the Physio Net/Speech Commands datasets.", "Figure 2: Ablation study on the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Sung Woo Park1, Kyungjae Lee2, Junseok Kwon3 1,3School of Computer Science and Engineering, Chung-Ang University, Korea 1,2,3Artificial Intelligence Graduate School, Chung-Ang University, Korea |
| Pseudocode | Yes | Algorithm 1 Neural Markov CSDE-TP |
| Open Source Code | No | We used open-source codes provided by the authors for comparison. |
| Open Datasets | Yes | Datasets. For the evaluations, we used Physio Net, Speech Commands, Beijing Air-Quality, and S&P500 Stock Market datasets. Refer to Appendix A.5 for data statistics and prepossessing procedures. |
| Dataset Splits | Yes | We used a half of time-series as the training dataset and the remaining parts as the test dataset.", "80% were used as training dataset and the remaining parts as the test dataset.", "used first 80% of temporal states to train the model and the remaining parts are used for prediction task. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computer specifications) used for running its experiments. |
| Software Dependencies | No | To estimate the gradient of the MBcond loss, we estimated numerical gradients with the auto-grad library in Pytorch (Paszke et al. (2019)). |
| Experiment Setup | Yes | For the running and terminal costs (l and Ψ, respectively), we used the l2 distance, i.e., l(s, x) = x ys 2 2 and Ψ(x) = x y T 2. In all experiments, γ is set to 0.95. The neural network structure for each agent control consists of 2-layers of fully-connected layers, where each module has 128 latent dimensions. The latent dimension of each Linear layer was set to 128 in all experiments except for the prediction task with the Air Quality dataset (= 64). |