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..
Regularization-free Diffeomorphic Temporal Alignment Nets
Authors: Ron Shapira Weber, Oren Freifeld
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 128 UCR datasets show that the proposed method outperforms contemporary methods despite not using a regularization. |
| Researcher Affiliation | Academia | 1Ben-Gurion University. Correspondence to: Ron Shapira Weber <EMAIL>, Oren Freifeld <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The JA training with an ICAE loss |
| Open Source Code | Yes | Our code is available at https: //github.com/BGU-CS-VIL/RF-DTAN. |
| Open Datasets | Yes | Extensive experiments on 128 UCR datasets show that the proposed method outperforms contemporary methods despite not using a regularization. |
| Dataset Splits | Yes | In all of the experiments, we used the train/test splits provided by the archive. ... To account for random initializations and the stochastic nature of DL training, in each of the 3 cases we performed 5 runs on each dataset and report both the median and best results; |
| Hardware Specification | Yes | We used a machine with 12 CPU-cores, 32Gb RAM, and an RTX 3090 graphic card. |
| Software Dependencies | Yes | The Py Torch TSAI implementation of the Inception Time was taken from (Oguiza, 2022). In the timing experiments ( 4.3), for DTW, DBA, and Soft DTW we used the tslearn package (Tavenard, 2017). |
| Experiment Setup | Yes | In all of our DTAN experiments, training was done via the Adam optimizer (Kingma & Ba, 2014) for 1500 epochs, batch size of 64, Np (the number of subintervals in the partition of Ω) was 16, and the scaling-andsquaring parameter (used by DIFW) was 8. |