main webpage
W Topic
Stochastic Gradient Descent
arXiv (Cornell University)
Learning Deep Networks from Noisy Labels with Dropout Regularization
2017
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting f…
Article
Open Stochastic Gradient Descent Open Article Page

Stochastic Gradient Descent

Optimization algorithm

Stochastic gradient descent (often abbreviated SGD ) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the very high computational burden, achieving faster iterations in exchange for a lower convergence rate.
Critical
Symbolic
Virtual
Narrative
Open Stochastic Gradient Descent Open Article Page
Exploring foci of:
arXiv (Cornell University)
Learning Deep Networks from Noisy Labels with Dropout Regularization
2017
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhap…
Open Article Page
Click Stochastic Gradient Descent Vs:
Softmax Function
Computer Science
Artificial Intelligence
Mnist Database
Deep Learning
Machine Learning
Perceptron
Open Article Page
Explore Stochastic Gradient Descent
WTopic
Stochastic Gradient Descent
arXiv (Cornell University)
Learning Deep Networks from Noisy Labels with Dropout Regularization
2017
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting f…
Article
Critical
Symbolic
Virtual
Narrative
Open Stochastic Gradient Descent Open Article Page