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].
Learning Multi-Task Sparse Representation Based on Fisher Information
Authors: Yayu Zhang, Yuhua Qian, Guoshuai Ma, Keyin Zheng, Guoqing Liu, Qingfu Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that, comparing with other methods, our proposed method can improve the performance for all tasks, and has high sparsity in multi-task learning. Experimental Studies This section presents a comparative analysis of the performance of FS with related works. The experimental results are presented in Table 1 and Table 2. |
| Researcher Affiliation | Academia | 1 Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China 2 School of Computer Science and Technology, North University of China, Taiyuan, Shanxi, 030051, China. 3 Department of Computer Science, City University of Hong Kong, Hong Kong, China 4 The City University of Hong Kong Shenzhen Research Institute, Shenzhen, China |
| Pseudocode | Yes | Pseudo-code are shown in algorithm 1 to algorithm 2. Algorithm 1: FSMTL Algorithm Framework. Algorithm 2: Updating Sparse Variable Set S. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | This paper conducts experiments on three multi-task datasets: DKL-mnist, Celeb A, and City Scapes. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, such as percentages or sample counts for a validation set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software dependencies with specific version numbers. |
| Experiment Setup | No | The paper has a section 'Optimization and Implementation Detail' which discusses updating parameters, but it does not specify concrete hyperparameters like learning rate, batch size, number of epochs, or optimizer settings for the experimental setup. |