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.
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Authors: Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 51,821 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,872 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 | Simulation results on a mixture of Gaussian validate our theoretical findings. Experiments on various pre-trained deep vision models show that naïve Bayes consistently converges faster as the number of data increases. |
| Researcher Affiliation | Collaboration | 1Gaoling School of AI, Renmin University of China; Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China 2School of Software, Shandong University 3Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua-Huawei Joint Center for AI, BNRist Center, THBI Lab, Tsinghua University 4Beijing Academy of Artificial Intelligence. Correspondence to: Chongxuan Li <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ML-GSAI/Revisiting-Dis-vs Gen-Classifiers. |
| Open Datasets | Yes | Experiments on various pre-trained deep vision models... We systematically compare logistic regression and naïve Bayes on the CIFAR10 and CIFAR100 datasets (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | We systematically compare logistic regression and naïve Bayes on the CIFAR10 and CIFAR100 datasets (Krizhevsky et al., 2009). |
| Hardware Specification | Yes | All experiments are done on a single Ge Force RTX 3090 GPU. |
| Software Dependencies | No | We train the logistic regression using scikit-learn s (Pedregosa et al., 2011) L-BFGS implementation... Res Net. We add the Res Net50 checkpoint released by Pytorch (Paszke et al., 2019). (Specific software versions are not provided). |
| Experiment Setup | Yes | For a fair comparison, we keep the linear evaluation setting in (Radford et al., 2021) throughout the experiments. Specially, we train the logistic regression using scikitlearn s (Pedregosa et al., 2011) L-BFGS implementation, with a maximum of 1000 iterations. We adjust the weight of ℓ2 regularization of logistic regression carefully... For each m, we obtain training samples randomly 5 times and record the mean test error of two models. |