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].
Task-Driven Common Representation Learning via Bridge Neural Network
Authors: Yao Xu, Xueshuang Xiang, Meiyu Huang5573-5580
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN. |
| Researcher Affiliation | Collaboration | Yao Xu,1,2 Xueshuang Xiang,1,2, Meiyu Huang1 1Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, 100190 2School of Aerospace Science and Technology, Xidian University, Xian, 710071 |
| Pseudocode | Yes | Algorithm 1 Training process of BNN |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We perform our experiments based on two datasets commonly used in the recent literature for CCA test: MNIST (Le Cun et al. 1998) half matching and X-Ray Microbeam Speech data (XRMB) (Westbury 1994). |
| Dataset Splits | Yes | The database contains 60,000 training images and 10,000 testing images of 28x28 pixels. (for MNIST) and In our experiment, 60,000 random samples are used for training and 10,000 for testing. (for XRMB). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments, only general mentions of 'lightweight convolution layers'. |
| Software Dependencies | No | The paper mentions using 'Scikitlearn' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The output dimension n of both networks is set to 50 for MNIST dataset, 112 for XRMB dataset, and the judging threshold Ξ³ is set to 0.5. and Define balance parameter Ξ±, learning rate Ξ· and NP ratio ΞΎ; from Algorithm 1. |