Near-Optimal Smoothing of Structured Conditional Probability Matrices
Authors: Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experiments Having expounded the theoretical merit of properly smoothing structered conditional probability matrices, we give a brief empirical study of its practical impact. We use both synthetic and real data. |
| Researcher Affiliation | Academia | Moein Falahatgar University of California, San Diego San Diego, CA, USA moein@ucsd.edu Mesrob I. Ohannessian Toyota Technological Institute at Chicago Chicago, IL, USA mesrob@ttic.edu Alon Orlitsky University of California, San Diego San Diego, CA, USA alon@ucsd.edu |
| Pseudocode | Yes | Algorithm: ADD1 2-SMOOTHED LOW-RANK |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., a specific repository link, an explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | tartuffe, a French text, train and test size: 9.3k words, vocabulary size: 2.8k words. genesis, English version, train and test size: 19k words, vocabulary size: 4.4k words brown, shortened Brown corpus, train and test size: 20k words, vocabulary size: 10.5k words All but the first one are readily available through the Python NLTK |
| Dataset Splits | Yes | In particular, half of the data was held out as a validation set, and for a range of different choices for m, the model was trained and its cross-entropy on the validation set was calculated. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For all these experiments, m = 50 and 200 iterations were performed. |