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Towards Understanding the Importance of Noise in Training Neural Networks
Numerous empirical evidence has corroborated that the noise plays a cruc...
09/07/2019 ∙ by Mo Zhou, et al. ∙
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On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
01/09/2020 ∙ by Minshuo Chen, et al. ∙
15
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Towards Understanding the Importance of Shortcut Connections in Residual Networks
Residual Network (ResNet) is undoubtedly a milestone in deep learning. R...
09/10/2019 ∙ by Tianyi Liu, et al. ∙
2
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Implementation of Training Convolutional Neural Networks
Deep learning refers to the shining branch of machine learning that is b...
06/03/2015 ∙ by Tianyi Liu, et al. ∙
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Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations
Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely ap...
02/14/2018 ∙ by Tianyi Liu, et al. ∙
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Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD...
06/04/2018 ∙ by Tianyi Liu, et al. ∙
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Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Extracting relations is critical for knowledge base completion and const...
08/21/2018 ∙ by Tianyi Liu, et al. ∙
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Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling
Stochastic simulation has been widely used to analyze the performance of...
12/24/2019 ∙ by Tianyi Liu, et al. ∙
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