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..
Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning
Authors: Cam-Tu Nguyen, Xiaoliang Wang, Jing Liu, Zhi-Hua Zhou
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on 2 multi-view datasets and 3 single-view datasets. |
| Researcher Affiliation | Academia | Cam-Tu Nguyen1,3, Xiaoliang Wang1, Jing Liu2 and Zhi-Hua Zhou1 1 National Key Laboratory for Novel Software Technology, Nanjing University, 210023, China 2 National Key Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, China 3 VNU-University of Engineering and Technology, Hanoi, Vietnam |
| Pseudocode | Yes | Algorithm 1 Generative Process for MIMLmix, Algorithm 2 Training with MIMLmix, Algorithm 3 Testing with MIMLmix |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code, nor does it provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Citeseerx-10k1 contains scientific papers in two views, i.e, content (v1) and citations (v2). Image CLEF (M uller et al. 2010) contains images with two views: visual (v1) and textual (v2). Here, we use the same subset that has been used in (Nguyen, Zhan, and Zhou 2013).1collected from http://citeseerx.ist.psu.edu/index ... Letter Carroll, MSRC-v2 were collected by (Briggs, Xiaoli, and Raich 2012); and IAPRTC-12 dataset was selected from (Escalante et al. 2010). |
| Dataset Splits | Yes | We conduct 30 times evaluation for Image CLEF, each time we use 1000 examples for training and 1000 examples for testing; 10-fold crossvalidation is conducted for the other datasets. |
| Hardware Specification | Yes | Table 4 shows training times of MIML methods on 3 large datasets on the same computer (CPU of 3.3Gz, 4GB memory). |
| Software Dependencies | No | The paper mentions using 'LIBSVM' and describes parameters like 'RBF kernel with C=23 and γ=.5' but does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | For MIMLmix methods, we set 0 = 0.1, K = 200 as default for both datasets, set = .3, = 5 for Citeseerx-10k; and = 10 and = 0 for Image CLEF. M3LDA is conducted with the same setting as in (Nguyen, Zhan, and Zhou 2013) on Image CLEF; and with K = 200, λ = .5, and the number of sampling iterations of 300 on Citeseerx-10k. |