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..
Structured Inference for Recurrent Hidden Semi-markov Model
Authors: Hao Liu, Lirong He, Haoli Bai, Bo Dai, Kun Bai, Zenglin Xu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Hao Liu1,2, Lirong He1, Haoli Bai3, Bo Dai4, Kun Bai5 and Zenglin Xu1 1 SMILE Lab, University of Electronic Science and Technology of China, Chengdu, China 2 Yingcai Honors College, University of Electronic Science and Technology of China, Chengdu, China 3 Dept. of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong 4 College of Computing, Georgia Institute of Technology, Atlanta, GA USA 5 Mobile Internet Group, Tencent Inc., Shenzhen, China |
| Pseudocode | Yes | Algorithm 1 Strucutured Inference Algorithm for SSNN |
| Open Source Code | No | The paper implements the proposed model based on Theano and Block & Fuel, citing the Theano website (1http://deeplearning.net/software/theano/). However, there is no explicit statement about releasing the source code for their specific methodology or a link to their own repository. |
| Open Datasets | Yes | We evaluate the SSNN on several datasets across multiple scenarios. Specifically, we first evaluate its performance of finding complex structures and estimating data likelihood on a synthetic dataset and two speech datasets (TIMIT & Blizard). Then we test the SSNN for learning segmentation and latent labels on Human activity [Reyes-Ortiz et al., 2016] dataset, Drosophila [Kain et al., 2013] and Physio Net Challenge[Springer et al., 2016]. |
| Dataset Splits | Yes | For Blizzard we split the data using 90% for training, 5% for validation and 5% for testing. For TIMIT we use the predefined test set for testing and split the rest of the data into 95% for training and 5% for validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper states 'We implement the proposed model based on Theano1 and Block & Fuel [Van Merri enboer et al., 2015]. The Adam [Kingma and Ba, 2014] optimizer was used in all experiments for our model.' However, no specific version numbers are provided for Theano, Block & Fuel, or Adam. |
| Experiment Setup | Yes | the temperature τ starts from a large value 0.1 and gradually anneals to 0.01. In the experiment, we fix the τ at small value 0.0001, and the maximum time steps T 10, 000. In the experiment, we find that annealing of temperature τ is important, we start from τ = 0.15 and anneal it gradually to 0.0001. During training we use back-propagation through time (BPTT) for 1 second. We set the truncation of max possible duration M to be 400 for all tasks. We also set the number of hidden states K to be the same as the ground truth. |