Unsupervised Sequence Classification using Sequential Output Statistics
Authors: Yu Liu, Jianshu Chen, Li Deng
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on real-world datasets demonstrate that the new unsupervised learning method gives drastically lower errors than other baseline methods. |
| Researcher Affiliation | Industry | Yu Liu , Jianshu Chen , and Li Deng Microsoft Research, Redmond, WA 98052, USA jianshuc@microsoft.com Citadel LLC, Seattle/Chicago, USA Li.Deng@citadel.com |
| Pseudocode | Yes | Algorithm 1 Stochastic Primal-Dual Gradient Method |
| Open Source Code | No | The code will be released soon. |
| Open Datasets | Yes | For the OCR task, we obtain our dataset from a public database UWIII English Document Image Database [27] |
| Dataset Splits | No | The paper mentions a 'train set' and 'test set' but does not specify explicit percentages or counts for training, validation, and test splits. For example, it states '153,221 characters for our OCR task' and 'total of 83,567 characters' for Spell-Corr, but doesn't detail how these are split into training, validation, and test subsets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | No | The paper mentions mini-batch sizes (10 to 10,000) and that hyperparameters were tuned, but does not provide a comprehensive list of specific hyperparameter values (e.g., learning rate, optimizer settings, number of epochs) or detailed training configurations. |