1.8k citations · 2.4k across the 40 of their papers we have counts for
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Nonparametric Neural Networks
George Philipp, Jaime G. Carbonell
Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks f…
The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions
George Philipp, Dawn Song, Jaime G. Carbonell
Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not th…
Asymmetric Variational Autoencoders
Guoqing Zheng, Yiming Yang, Jaime Carbonell
Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower Bound (ELBO) of the true data…
Convolutional Normalizing Flows
Guoqing Zheng, Yiming Yang, Jaime Carbonell
Bayesian posterior inference is prevalent in various machine learning problems. Variational inference provides one way to approximate the posterior distribution, however its expres…
Gradient-based Inference for Networks with Output Constraints
Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick +2
Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output stru…
Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network
Adams Wei Yu, Lei Huang, Qihang Lin +2
In this paper, we propose a generic and simple strategy for utilizing stochastic gradient information in optimization. The technique essentially contains two consecutive steps in e…