Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Topic Modeling with Continual Lifelong Learning
Authors: Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schuetze
ICML 2020 | Venue PDF | 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. |