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..
Structural Learning with Amortized Inference
Authors: Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, Dan Roth
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entity-relation extraction task. Our experiments aim to show the following properties: (1) the amortization techniques significantly reduce the number of inference solver calls, and hence the training time is reduced; (2) with an adaptation on ϵ, the model converges to the same objective function value; (3) with the help of pre-cached examples, we achieve higher amortization ratio and further speed up the training process. Table 1: Ratio of inference engine calls, inference time, inference speedup, final negative dual objective function value ( D(α) in Eq. (6)) and the test performance of each method. |
| Researcher Affiliation | Academia | Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, and Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1 A Dual Coordinate Descent Method with Amortized Inference |
| Open Source Code | No | The paper states: 'We implemented our algorithms based on a publicly available Structured SVM package4 in JAVA' and provides a footnote link to 'http://cogcomp.cs.illinois.edu/page/software view/JLIS'. This link points to a general software page for a package they used, not specifically the source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the annotated corpus from Roth and Yih (2007), which consists of 5,925 sentences. ... We use a dataset, scene3, with 6 labels and 1,211 instances to demonstrate the performance of our method. ... 3Available at http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/ datasets |
| Dataset Splits | Yes | performing a 5-fold cross validation for picking the best regularization parameter C for structured SVM on the entity-relation recognition task. |
| Hardware Specification | Yes | We implemented our algorithms based on a publicly available Structured SVM package4 in JAVA and conducted experiments on a machine with Xeon E5-2440 processors. |
| Software Dependencies | No | The paper mentions 'JAVA' and 'Gurobi' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Unless otherwise stated, we show the performance of training Structured SVM model with C = 0.1 with stopping condition δ = 0.1. For Perceptron, we used an averaged Perceptron implementation in JAVA and set the max number of iterations to 100. ... We choose C from {0.01, 0.5 0.1, 0.5, 1}, running 5 folds for each, for a total of 25 runs. δ is set to 0.1 for all folds. |