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..
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
Authors: Ting Huang, Gehui Shen, Zhi-Hong Deng
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. |
| Researcher Affiliation | Academia | Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University EMAIL |
| Pseudocode | No | The paper describes the model architecture and equations but does not provide any pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | We provide a github link https://github.com/Anonymized User/appendixfor-leap-LSTM. |
| Open Datasets | Yes | We use five freely available large-scale data sets introduced by [Zhang et al., 2015], which cover several classification tasks (see Table 1). |
| Dataset Splits | Yes | For each data set, we randomly select 10% of the training set as the development set for hyperparameter selection and early stopping. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using GloVe embeddings and Adam optimizer but does not specify software versions for libraries or frameworks like Python, PyTorch, TensorFlow, etc. |
| Experiment Setup | Yes | Dimensions {h, d, p, f, s, h } are set to {300, 300, 300, 200, 20, 20} respectively. The sizes of CNN filters are {[3, 300, 1, 60], [4, 300, 1, 60], [5, 300, 1, 60]}. The temperature τ is always 0.1. For λ and rt, the hyperparameters of the penalty term, different settings are applied, which depends on our desired skip rate. Throughout our experiments, we use a size of 32 for minibatches. We use Adam [Kingma and Ba, 2014] to optimize all trainable parameters with a initial learning rate 0.001. |