Rapid Performance Gain through Active Model Reuse
Authors: Feng Shi, Yu-Feng Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the effectiveness of Ac MR. In this section, we first give the experimental setup and then show the evaluation of our proposal compared to several state-of-the-art algorithms on a number of real-world tasks. |
| Researcher Affiliation | Academia | Feng Shi and Yu-Feng Li National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {shif, liyf}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The learning algorithm for ACMR |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | The text classification task is collected from 20 Newsgroups1. 1https://www.cse.ust.hk/TL/ and The last task is a spam detection problem, and we use the dataset obtained from ECML PAKDD Discovery challenge2 to verify whether our method can help improve the performance. 2http://ecmlpkdd2006.org/challenge.html |
| Dataset Splits | No | For each task, we randomly divide the data into two parts: 75% as the unlabeled pool, and the rest 25% as the test set. (Does not mention a distinct validation set) |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'hinge loss and logistic regression' and refers to 'λ > 0' in the algorithm, but does not provide specific numerical values for hyperparameters like learning rate, batch size, or other detailed training configurations. |