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..
Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction
Authors: Yue Deng, Yilin Shen, Hongxia Jin
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the performance and generalization ability of ACA on diverse learning tasks including multi-label classification, attributes prediction and sequence-to-sequence generation. |
| Researcher Affiliation | Industry | Yue Deng, Yilin Shen and Hongxia Jin AI Research Center, Samsung Research America EMAIL |
| Pseudocode | Yes | Algorithm 1: ACA optimization |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or providing a link to a code repository. |
| Open Datasets | Yes | We evaluate the performances of ACA on multiple-label classification (MLC) for documents modeling on bibtex and bookmark datasets[Loza Menc ıa and F urnkranz, 2008]. The bibtex dataset contains 7, 395 samples from 159 classes; and bookmark dataset contains 87, 856 samples within 208 classes. We also include the delicious dataset [Tsoumakas et al., 2008] in our experiment that contains 16, 105 samples in 983 classes. |
| Dataset Splits | Yes | We randomly sample 20% of the whole data for testing and the other 80% data are for training and validation. |
| Hardware Specification | No | All reported time is calculated by running our algorithm with Tensor Flow on 8 GPUs. |
| Software Dependencies | No | We implement all three network structures with Tensor Flow and adopt the ADAM optimizer [Kingma and Ba, 2014] for optimization. |
| Experiment Setup | Yes | In ACA, the dimensions for help vector vi and comparability fusion layer h(xi,yi) are both fixed as 64. The recurrent steps T is fixed as 5. |