10 citations · 28 across the 4 of their papers we have counts for
5 papers
Deep Diffeomorphic Normalizing Flows
Hadi Salman, Payman Yadollahpour, Tom Fletcher +1
The Normalizing Flow (NF) models a general probability density by estimating an invertible transformation applied on samples drawn from a known distribution. We introduce a new typ…
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh +3
We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning…
Nonparametric Spherical Topic Modeling with Word Embeddings
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan +1
Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics…
Diversifying Sparsity Using Variational Determinantal Point Processes
Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza +3
We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of fea…