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].
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Authors: Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to two personalized attribute learning datasets. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Information Security, Institute of Information Engineering, CAS 2School of Cyber Security, University of Chinese Academy of Sciences 3Key Lab. of Intelligent Information Processing, Institute of Computing Technology, CAS 4School of Computer Science and Tech., University of Chinese Academy of Sciences 5Key Laboratory of Big Data Mining and Knowledge Management, CAS 6Peng Cheng Laboratory |
| Pseudocode | No | The paper describes an |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions the use of 'simulation dataset, three real-world benchmark datasets and further applied to two personalized attribute learning datasets' but does not provide concrete access information (link, DOI, repository, or formal citation with author/year) for these datasets. |
| Dataset Splits | Yes | Except for the Simulated Dataset, the train/valid/test ratio is fixed as 70%/15%/15%. |
| Hardware Specification | No | The paper states, |
| Software Dependencies | Yes | All the experiments are run with MATLAB 2016b and a Ubuntu 16.04 system. |
| Experiment Setup | No | The paper mentions that |