Neural Topic Modeling with Continual Lifelong Learning
Authors: Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schuetze
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experimental results show improved topic modeling on future task while retaining past learning, quantified by information retrieval, topic coherence and generalization capabilities. |
| Researcher Affiliation | Collaboration | 1Corporate Technology, Siemens AG Munich, Germany 2CIS, University of Munich (LMU) Munich, Germany. |
| Pseudocode | Yes | Algorithm 1 Lifelong Neural Topic Modeling using Doc NADE |
| Open Source Code | Yes | Code: https://github.com/pgcool/ Lifelong-Neural-Topic-Modeling |
| Open Datasets | Yes | AGnews, TMN, R21578 and 20NS (20News Groups), and three short-text (low-resource) corpora ΩT +1 as future tasks T + 1: 20NSshort, TMNtitle and R21578title. |
| Dataset Splits | No | The paper states 'PPL (Algorithm 2: line #10) is used for model selection and adjusting parameters Θt and hyperparameters Φt.' and refers to a 'test set', but does not explicitly provide specific train/validation/test dataset split percentages or sample counts for a distinct validation set. |
| Hardware Specification | Yes | Figures 3, 4 and 5 show average runtime (r-time) for each training epoch of different LNTM approaches, run on an NVIDIA Tesla K80 Processor (RAM: 12 GB) to a maximum of 100 epochs. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python version, library versions like TensorFlow or PyTorch). |
| Experiment Setup | Yes | Figures 3, 4 and 5 show average runtime (r-time) for each training epoch of different LNTM approaches, run on an NVIDIA Tesla K80 Processor (RAM: 12 GB) to a maximum of 100 epochs. |