4 papers
Divergence Results and Convergence of a Variance Reduced Version of ADAM
Ruiqi Wang, Diego Klabjan
Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications…
Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis
Adam Sandler, Diego Klabjan, Yuan Luo
We analyze large, multi-dimensional, sparse counting data sets, finding unsupervised groups to provide unique insights into genetic data. We create gene and biological pathway grou…
Non-Convex Optimization with Spectral Radius Regularization
Adam Sandler, Diego Klabjan, Yuan Luo
We develop regularization methods to find flat minima while training deep neural networks. These minima generalize better than sharp minima, yielding models outperforming baselines…
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Hanqing Li, Diego Klabjan, Jean Utke
This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simp…