Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy (86.14 ) on the SNLI dataset and (77.05 ) on SciTail.
Dropout rates are also crucial for the NLI models @cite_12 . Even the models which apply dropout at the same locations vary dropout rates.
@cite_12
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Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods. Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout. We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy. We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the mini-batch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments.