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..
Learning Precise Temporal Point Event Detection with Misaligned Labels
Authors: Julien Schroeter, Kirill Sidorov, David Marshall9505-9514
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In order to demonstrate the effectiveness and flexibility of our approach, a broad range of challenging experiments are conducted. |
| Researcher Affiliation | Academia | Julien Schroeter, Kirill Sidorov, David Marshall School of Computer Science & Informatics, Cardiff University, UK EMAIL |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available1. 1 https://github.com/Schroeter Julien/AAAI-2021-Learning Precise-Temporal-Point-Event-Detection-with-Misaligned-Labels |
| Open Datasets | Yes | Golf DB: A Video Database for Golf Swing Sequencing (Mc Nally et al. 2019), puff Marker dataset (Saleheen et al. 2015), MAPS database (Emiya, Badeau, and David 2010), IDMT-SMT-Drums dataset (Dittmar and Gärtner 2014) |
| Dataset Splits | Yes | The (4-fold) cross-validated mean accuracy is reported. (Table 1 caption) and The results, produced using ten 6-fold (leave-one-patient-out) cross-validations are summarized in Table 2. |
| Hardware Specification | No | We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for this research. (This only specifies 'a GPU' from NVIDIA, not a specific model, memory, or other hardware details.) |
| Software Dependencies | No | The paper mentions the 'mir eval library' (Raffel et al. 2014) but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | (SM = 100ms, ατ = max(min( τ 105 , .9), .2).) (Section 6.3) and (learning rate: 10 4, batch size: 32, iterations: 1.5 105, sample length: 1.5s) (Section 6.4) |