970 citations · 3k across the 88 of their papers we have counts for
7 papers · 1 filter
Gauss quadrature for matrix inverse forms with applications
Chengtao Li, Suvrit Sra, Stefanie Jegelka
We present a framework for accelerating a spectrum of machine learning algorithms that require computation of bilinear inverse forms , where is a positive defin…
Deep Metric Learning via Lifted Structured Feature Embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka +1
Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolution…
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka +1
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world d…
Optimization as Estimation with Gaussian Processes in Bandit Settings
Zi Wang, Bolei Zhou, Stefanie Jegelka
Recently, there has been rising interest in Bayesian optimization -- the optimization of an unknown function with assumptions usually expressed by a Gaussian Process (GP) prior. We…
Efficient Sampling for k-Determinantal Point Processes
Chengtao Li, Stefanie Jegelka, Suvrit Sra
Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by e…
Convex Optimization for Parallel Energy Minimization
K. S. Sesh Kumar, Alvaro Barbero, Stefanie Jegelka +2
Energy minimization has been an intensely studied core problem in computer vision. With growing image sizes (2D and 3D), it is now highly desirable to run energy minimization algor…
Inferring and Learning from Neuronal Correspondences
Ashish Kapoor, E. Paxon Frady, Stefanie Jegelka +2
We introduce and study methods for inferring and learning from correspondences among neurons. The approach enables alignment of data from distinct multiunit studies of nervous syst…