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..
Binary Coding based Label Distribution Learning
Authors: Ke Wang, Xin Geng
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five benchmark datasets validate the superiority of BCLDL over several state-of-the-art LDL methods. |
| Researcher Affiliation | Academia | MOE Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | Yes | Algorithm 1 BC-LDL: Train Algorithm |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed algorithms on five different datasets: s-BU 3DFE (scores-Binghamton University 3D Facial Expression) [Zhou et al., 2015], COPM (Crowd Opinion Prediction on Movies) [Geng and Hou, 2015], Twitter LDL [Yang et al., 2017], Ren-CECps [Quan and Ren, 2010] and MORPH [Ricanek and Tesafaye, 2006]. |
| Dataset Splits | Yes | All the results are averaged over 10-fold cross validation in terms of both accuracy and time cost. |
| Hardware Specification | Yes | All the experiments are carried on a PC with Intel (R) Core (TM) CPU i5-6300@2.30GHz and 12GB RAM. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | On s-BU 3DFE, k in BC-LDL is set to 20 and code length is set to 32 bits. The maximum iteration steps in BFGS-LDL is 300 and IIS-LDL is 100. k for AA-k NN is set to 20. On COPM, k in BC-LDL is 30 and code length is 128 bits. The maximum iteration steps in BFGS-LDL is 100 and IIS-LDL is 20. k in AA-k NN is 10. On Twitter LDL, k in BC-LDL is set to 10 and code length is 256 bits. The maximum iteration steps in BFGS-LDL is 300 and IIS-LDL is 50. k in AA-k NN is 10. On Ren-CECps, k in BC-LDL is 20 and code length is set to 64 bits. The maximum iteration steps in BFGS-LDL is 200 and IIS-LDL is 100. k in AAk NN is set to 20. On MORPH, k in BC-LDL is set to 50 and code length is 256 bits. The maximum iteration steps in BFGS-LDL is 100 and IIS-LDL is 20. k in AA-k NN is set to 10. On the five datasets, the insensitivity parameter ε of LDSVR is set to 0.1 and the number of hidden-layer neurons of CPNN is set to 50. |