Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Large-margin Weakly Supervised Dimensionality Reduction
Authors: Chang Xu, Dacheng Tao, Chao Xu, Yong Rui
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,139 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,397 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets demonstrate the significance of studying dimensionality reduction in the weakly supervised setting and the effectiveness of the proposed framework. |
| Researcher Affiliation | Collaboration | Key Lab. of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney 2007, Australia Microsoft Research, No. 5, Dan Ling Street, Haidian District, Beijing 10080, China |
| Pseudocode | No | The paper describes algorithms but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology was found. |
| Open Datasets | Yes | UMIST face dataset (Graham & Allinson, 1998); USPS digit dataset (Hull, 1994); Movie Lens dataset 1http://grouplens.org/datasets/movielens/; The book-crossing dataset 2http://grouplens.org/datasets/book-crossing/; Three datasets 3 (Table 1) |
| Dataset Splits | Yes | We used 70% of the movies rated by each user for training and the remaining 30% for testing.; The pairwise accuracies for different algorithms were evaluated on each dataset in a five-fold cross-validation experiment |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments are mentioned. |
| Software Dependencies | No | The paper mentions names of algorithms/techniques (e.g., 'Rank SVM', 'LPP', 'LFDA', 'LMCA', 'GBRT') and references papers describing them, but does not specify software dependencies with version numbers (e.g., specific libraries or frameworks like 'PyTorch 1.9'). |
| Experiment Setup | No | The paper discusses parameters like 'constant margins γ1 and γ2', 'constant C', 'smooth parameter σ', 'learning rate α', and 'limited depth p' within the algorithm descriptions. However, it does not provide concrete numerical values for these hyperparameters or other system-level training settings used in the experiments. |