Altitude Training: Strong Bounds for Single-Layer Dropout
Authors: Stefan Wager, William Fithian, Sida Wang, Percy Liang
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments and Discussion; Synthetic Experiment; Sentiment Classification We also examined the performance of dropout as a function of training set size on a document classification task. Figure 3a shows results on the Polarity 2.0 task [17]... We also ran experiments on a larger IMDB dataset [18]... |
| Researcher Affiliation | Academia | Stanford University, Stanford, CA-94305, USA |
| Pseudocode | No | The paper describes algorithms conceptually but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | Figure 3a shows results on the Polarity 2.0 task [17], where the goal is to classify positive versus negative movie reviews on IMDB. We also ran experiments on a larger IMDB dataset [18] with training and test sets of size 25,000 each and approximately 300,000 features. |
| Dataset Splits | No | We divided the dataset into a training set of size 1,200 and a test set of size 800; training and test sets of size 25,000 each. The paper mentions training and testing sets but does not specify a separate validation split or cross-validation strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a “bag-of-words logistic regression model” and performing “Mc Nemar’s test” but does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper mentions recalibrating the intercept and using a bag-of-words logistic regression model but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |