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..
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
Authors: Ron Shapira Weber, Shahar Benishay, Andrey Lavrinenko, Shahaf E. Finder, Oren Freifeld
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Time Point consistently achieves faster and more accurate alignments than standard DTW, making it a scalable solution for time-series analysis. Our code is available at https://github.com/ BGU-CS-VIL/Time Point. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Ben Gurion University of the Negev (BGU). 2Data Science Research Center, BGU. 3School of Brain sciences and Cognition, BGU. Correspondence to: Ron Shapira Weber <EMAIL>. |
| Pseudocode | No | The paper describes the Time Point architecture and loss functions in sections 4 and 4.4, and the data generation process in section 3, but does not present a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/ BGU-CS-VIL/Time Point. |
| Open Datasets | Yes | We evaluate its generalization to real-world data using the UCR Time Series Archive (Dau et al., 2019). |
| Dataset Splits | Yes | We use the original train-test splits provided by the archive. |
| Hardware Specification | Yes | Training is performed on a single NVIDIA RTX6000 GPU with 48 GB of memory. |
| Software Dependencies | No | Our model, implemented in Py Torch, has a total of 200K trainable parameters. We have adopted the 2D WT-Conv layer from the official implementation (Finder et al., 2024) to 1D inputs. |
| Experiment Setup | Yes | Training is performed on a single NVIDIA RTX6000 GPU with 48 GB of memory. The model converges within approximately 100,000 iterations and 20 hours, with a batch size of 512, and the Adam W optimizer (Loshchilov, 2017) with a learning rate of 1 10 4 with cosine learning rate scheduler. The encoder consists of 4 layers with a number of kernels = [128, 128, 256, 256]. |