Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
Authors: Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results on several benchmark data sets, comparing our model with various state-of-the- art methods. 1 Introduction Multi-label learning refers to the problem setting in which the goal is to assign to an object (e.g., a video, image, or webpage) a subset of labels (e.g., tags) from a (possibly very large) set of labels. ... We evaluate the proposed multi-label learning framework on four benchmark multi-label data sets bibtex, delicious, compphys, eurlex [25], with their statistics summarized in Table 1. |
| Researcher Affiliation | Academia | Piyush Rai , Changwei Hu , Ricardo Henao , Lawrence Carin CSE Dept, IIT Kanpur ECE Dept, Duke University piyush@cse.iitk.ac.in, {ch237,r.henao,lcarin}@duke.edu |
| Pseudocode | No | The paper describes the inference steps (Gibbs Sampling and Expectation Minimization) in detailed text but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the proposed multi-label learning framework on four benchmark multi-label data sets bibtex, delicious, compphys, eurlex [25], with their statistics summarized in Table 1. |
| Dataset Splits | No | The paper mentions 'Training set' and 'Test set' for the datasets but does not explicitly specify a separate 'validation' dataset split or cross-validation strategy for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | The EM algorithms were run for 1000 iterations and they converged in all the cases. ... For each method, we set K = 0.4L. ... We run each experiment for 100 iterations (using EM for the inference) ... We run each inference method only for 100 iterations. For EM, we try two settings: EM with an exact M step for W, and EM with an approximate M step where we run 2 steps of conjugate gradient (CG). |