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 [1].
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond
Authors: Shiji Zhou, Wenpeng Zhang, Jiyan Jiang, Wenliang Zhong, Jinjie GU, Wenwu Zhu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically verify our proposal in simulation and deep multi-task learning tasks, where we observe comparable or better performance. |
| Researcher Affiliation | Collaboration | Shiji Zhou Tsinghua University EMAIL Wenpeng Zhang Ant Group EMAIL Jiyan Jiang Tsinghua University EMAIL Wenliang Zhong Ant Group EMAIL Jinjie Gu Ant Group EMAIL Wenwu Zhu Tsinghua University EMAIL |
| Pseudocode | Yes | Algorithm 1 Correlation-Reduced Stochastic Multi-objective Gradient Manipulation (CR-MOGM) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | Multi MNIST dataset [37] with 60K examples. ... City Scapes. |
| Dataset Splits | No | The paper mentions "train and test losses" but does not provide specific percentages, sample counts, or explicit descriptions of how the datasets were split for training, validation, or testing. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using SGD and Adam optimizers and specific architectures like Le Net and Seg Net, but does not provide version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We initialize x0 = (0, 0.4) and use SGD as the optimizer. To enable fair comparison, the adaptive stepsize is set with k = 0.006/ k for all the algorithms, and the momentum parameter for smoothing the composite weights is set with the suggested parameter setting in Corollary 1. |