Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
نویسندگان
چکیده
منابع مشابه
A Bayesian encourages dropout
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 enco...
متن کاملHigh Dimensional Bayesian Optimization using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited “active” variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize onl...
متن کاملDropout as a Bayesian Approximation: Appendix
We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model. This interpretation might offer an explanation to some of dropout’s key properties, such as its robustness to overfitting. Our interpretation allows us to reason about uncertainty in deep learning...
متن کاملModel-Free Adaptive Control Algorithm with Data Dropout Compensation
The convergence of model-free adaptive control MFAC algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to up...
متن کاملA Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response
In this paper, multivariate skew-normal distribution is em- ployed for analyzing an outcome based dropout model for repeated mea- surements with non-random dropout in skew regression data sets. A probit regression is considered as the conditional probability of an ob- servation to be missing given outcomes. A simulation study of using the proposed methodology and comparing it with a semi-parame...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2021
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2021.3083089