3 citations · 6 across the 4 of their papers we have counts for
6 papers
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
Max Torop, Sandesh Ghimire, Wenqian Liu +5
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OO…
Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips
Ashutosh Singh, Christiana Westlin, Hedwig Eisenbarth +7
For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e.g., f…
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar +3
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to a…
Rate-Regularization and Generalization in VAEs
Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan +3
Variational autoencoders optimize an objective that combines a reconstruction loss (the distortion) and a KL term (the rate). The rate is an upper bound on the mutual information,…
Can VAEs Generate Novel Examples?
Alican Bozkurt, Babak Esmaeili, Dana H. Brooks +2
An implicit goal in works on deep generative models is that such models should be able to generate novel examples that were not previously seen in the training data. In this paper,…
Structured Disentangled Representations
Babak Esmaeili, Hao Wu, Sarthak Jain +6
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disen…