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..
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
Authors: Wei Tang, Weijia Zhang, Min-Ling Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China; School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia |
| Pseudocode | Yes | Algorithm 1 summarizes the complete procedure of DEMIPL. |
| Open Source Code | Yes | Additionally, the code of DEMIPL, the benchmark datasets, and the real-world dataset are publicly available at http://palm.seu.edu.cn/zhangml/. |
| Open Datasets | Yes | We utilize four benchmark MIPL datasets stemming from MIPLGP literature [36], i.e., MNIST-MIPL, FMNIST-MIPL, Birdsong-MIPL, and SIVAL-MIPL from domains of image and biology [48 51]. Additionally, the code of DEMIPL, the benchmark datasets, and the real-world dataset are publicly available at http://palm.seu.edu.cn/zhangml/. |
| Dataset Splits | No | The paper states 'We conduct ten runs of random train/test splits with a ratio of 7 : 3 for all datasets' but does not explicitly mention a separate validation split or its proportion. |
| Hardware Specification | Yes | DEMIPL is implemented using Py Torch [59] on a single Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [59]' but does not provide a specific version number for PyTorch or other software dependencies with their versions. |
| Experiment Setup | Yes | We employ the stochastic gradient descent (SGD) optimizer with a momentum of 0.9 and weight decay of 0.0001. The initial learning rate is chosen from a set of {0.01, 0.05} and is decayed using a cosine annealing method [60]. The number of epochs is set to 200 for the SIVAL-MIPL and CRC-MIPL datasets, and 100 for the remaining three datasets. The value of Îťa is selected from a set of {0.0001, 0.001}. |