Bidirectional Convolutional Poisson Gamma Dynamical Systems
Authors: wenchao chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a variety of document corpora demonstrate that the proposed models can extract expressive multi-level latent representations, including interpretable phrase-level topics and sentence-level temporal transitions as well as discriminative document-level features, achieving state-of-the-art document categorization performance while being memory and computation efficient. |
| Researcher Affiliation | Academia | Wenchao Chen , Chaojie Wang , Bo Chen , Yicheng Liu, Hao Zhang National Laboratory of Radar Signal Processing Xidian University, Xi an, Shaanxi 710071, China wcchen_xidian@163.com, xd_silly@163.com, bchen@mail.xidian.edu.cn, moooooore66@gmail.com, zhanghao_xidian@163.com Mingyuan Zhou Mc Combs School of Business The University of Texas at Austin Austin, TX 78712, USA mingyuan.Zhou@mccombs.utexas.edu |
| Pseudocode | No | The paper describes mathematical formulations and generative processes but does not include explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Python (Py Torch) code is provided at https://github.com/Bo Chen Group/BCPGDS. |
| Open Datasets | Yes | We evaluate the effectiveness of our model on five large corpora, including ELEC, IMDB-2, Reuters, Yelp 2014, and IMDB-10, which are described in detail in the Appendix. |
| Dataset Splits | Yes | We evaluate our model with different amount of labeled data (5%, 10%, 25%, or 50%), besides the whole training set being unlabeled data. |
| Hardware Specification | Yes | Table 3: Comparison of testing times (seconds) with batch-size 128 on two RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions "Python (Py Torch) code is provided" but does not specify version numbers for Python, PyTorch, or other relevant libraries. |
| Experiment Setup | Yes | We fix the hyperparameters of our models as τ0 = 1, ϵ0 = 0.1, γ0 = 0.1, η = 0.05 for all experiments. |