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..
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
Authors: JAEHOON LEE, Jinsung Jeon, Sheo yon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Kook Seungji, Noseong Park
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with benchmark datasets, the improvement ratio by our method is up to 75% in terms of various classification and forecasting evaluation metrics. |
| Researcher Affiliation | Academia | Jaehoon Lee, Jinsung Jeon, Sheoyon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Seungji Kook, and Noseong Park Yonsei University Seoul, South Korea EMAIL |
| Pseudocode | Yes | Algorithm 1: How to train LORD-NRDE |
| Open Source Code | Yes | Our code is available in https://github.com/leejaehoon2016/LORD. |
| Open Datasets | Yes | We use six real-word dataset which all contain very long time-series samples. There are 3 classification datasets in the University of East Anglia (UEA) repository (Tan & Webb): Eigen Worms, Counter Movement Jump, and Self Regulation SCP2, and 3 forecasting datasets in Beth Israel Deaconess Medical Centre (BIDMC) which come from the TSR archive (Tan & Webb): BIDMCHR, BIDMCRR, BIDMCSp O2. We refer to Appendix B for detail of datasets. |
| Dataset Splits | Yes | Input: Training data Dtrain, Validating data Dval, Maximum iteration numbers max iter AE and max iter T ASK |
| Hardware Specification | Yes | Our software and hardware environments are as follows: UBUNTU 18.04 LTS, PYTHON 3.7.10, PYTORCH 1.8.1, CUDA 11.4, and NVIDIA Driver 470.42.01, i9 CPU, and NVIDIA RTX A6000. |
| Software Dependencies | Yes | Our software and hardware environments are as follows: UBUNTU 18.04 LTS, PYTHON 3.7.10, PYTORCH 1.8.1, CUDA 11.4, and NVIDIA Driver 470.42.01, i9 CPU, and NVIDIA RTX A6000. |
| Experiment Setup | Yes | The number of layers in the encoder, decoder and main NRDE, Ng, Nf, and No of Eqs. 9 to 11, are in {2, 3}. The hidden sizes, hg, hf, and ho of Eqs. 9 to 11, are in {32, 64, 128, 192}. The coefficients of the L2 regularizers in Eqs. 13 and 14 are in {1 10 5, 1 10 6}. The coefficient of the embedding regularizer, ce in Eq. 13 is in {0, 1, 10}. The max iteration numbers, max iter AE and max iter T ASK in Alg. 1, are in {400, 500, 1000, 1500, 2000}. The learning rate of the pre-training and main-training is 1 10 3. |