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..
GO4Align: Group Optimization for Multi-Task Alignment
Authors: Jiayi Shen, Qi Wang, Zehao Xiao, Nanne van Noord, Marcel Worring
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results on diverse benchmarks demonstrate our method s performance superiority with even lower computational costs. |
| Researcher Affiliation | Academia | 1University of Amsterdam, Amsterdam, the Netherlands 2Department of Automation, Tsinghua University, Beijing, China |
| Pseudocode | Yes | The pseudo-code of GO4Align is provided in Appendix A. |
| Open Source Code | Yes | We provide the code for our method to encourage follow-up work.3 https://github.com/autumn9999/GO4Align |
| Open Datasets | Yes | We conduct experiments on four benchmarks commonly used in multi-task optimization literature [6, 13, 14, 16]: NYUv2 [34], City Scapes [35], QM9 [36], and Celeb A [37]. |
| Dataset Splits | Yes | This work follows the same experimental setting used in Nash MTL [13] and FAMO [14], including the dataset partition for training, validation, and testing. Table 6: Benchmark partition for training, validation, and testing. NYUv2 1449 795 N/A 654 City Scapes 3475 2975 N/A 500 QM9 130k 110k 10k 10k Celeb A 202,599 162,770 19,867 19,962 |
| Hardware Specification | Yes | We conduct all experiments on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch Geometric' and 'Adam as the optimizer', but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train each method for 200 epochs with an initial learning rate of 1e 4 and reduce the learning rate to 5e 5 after 100 epochs. The architecture is Multi-Task Attention Network (MTAN) [6] built upon Seg Net [45]. Batch sizes for NYUv2 and City Scapes are set as 2 and 8 respectively. We train each method for 300 epochs with a batch size of 120 and search for the best learning rate in {1e 3,5e 4,1e 4}. We take Reduce On Plateau [13] as the learning-rate scheduler to decrease the lr once the validation overall performance stops improving. We train the model for 15 epochs with a batch size of 256. We adopt Adam as the optimizer with a fixed learning rate of 1e 3. |