Adaptive Dropout Rates for Learning with Corrupted Features
Authors: Jingwei Zhuo, Jun Zhu, Bo Zhang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on various datasets demonstrate the effectiveness on avoiding extensive tuning and sometimes improving the performance due to its flexibility. |
| Researcher Affiliation | Academia | Jingwei Zhuo, Jun Zhu, Bo Zhang Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Center for Bio-Inspired Computing Research, Tsinghua University, Beijing, 100084, China Jiangsu Collaborative Innovation Center for Language Ability, Xuzhou, 221009, China zhuojw10@mails.tsinghua.edu.cn, dcszj@tsinghua.edu.cn, dcszb@tsinghua.edu.cn |
| Pseudocode | No | The paper describes the algorithm steps in paragraph text (e.g., 'our algorithm iteratively performs the following steps: For η: update... For q(λ): infer... For θ: plug...'), but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of its source code. |
| Open Datasets | Yes | The Amazon review datasets... [Blitzer et al., 2007]; The MEMset Donor dataset... Available at: http://genes.mit.edu/burgelab/maxent/ssdata/; 20 Newsgroup dataset... Available at: http://people.csail.nut.edu/jrennie/20Newsgroups/; MNIST dataset... Available at: http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | Yes | Here we follow the setup in [Meier et al., 2008], which splits the original training set into a balanced training set (5,610 true and 5,610 false instances) and an unbalanced validation set (2,805 true and 59,804 false instances) which has the same true/false ratio as the testing set. |
| Hardware Specification | No | The paper mentions 'a single thread' in the context of training time, but does not provide any specific hardware details like GPU/CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions implementation 'in C++ using L-BFGS methods', but no specific version numbers for the programming language, compiler, or any libraries/solvers are provided. |
| Experiment Setup | Yes | We run the algorithms with 640 iterations, which are sufficiently large for all methods to converge. We adopt the one-vs-all strategy [Rifkin and Klautau, 2004]... we set different weights for the contribution of the positive and negative instances in Eq. (15) corresponding to the ratio r of numbers between the positive and negative instances to make a balance |