
βAnnealed Variational Autoencoder for glitches
Gravitational wave detectors such as LIGO and Virgo are susceptible to v...
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Substitutional Neural Image Compression
We describe Substitutional Neural Image Compression (SNIC), a general ap...
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Realtime Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semanti...
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Piecewise Linear Regression via a Difference of Convex Functions
We present a new piecewise linear regression methodology that utilizes f...
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Deep Divergence Learning
Classical linear metric learning methods have recently been extended alo...
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Joint Bilateral Learning for Realtime Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic s...
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Protecting Neural Networks with Hierarchical Random Switching: Towards Better RobustnessAccuracy Tradeoff for Stochastic Defenses
Despite achieving remarkable success in various domains, recent studies ...
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Learning Bregman Divergences
Metric learning is the problem of learning a taskspecific distance func...
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Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Existing automatic music generation approaches that feature deep learnin...
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WNet: A Deep Model for Fully Unsupervised Image Segmentation
While significant attention has been recently focused on designing super...
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Dynamic Clustering Algorithms via SmallVariance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the...
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Stable Distribution Alignment Using the Dual of the Adversarial Distance
Methods that align distributions by minimizing an adversarial distance b...
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Combinatorial Topic Models using SmallVariance Asymptotics
Topic models have emerged as fundamental tools in unsupervised machine l...
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A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models
Conjugate pairs of distributions over infinite dimensional spaces are pr...
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Revisiting Kernelized LocalitySensitive Hashing for Improved LargeScale Image Retrieval
We present a simple but powerful reinterpretation of kernelized locality...
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PowerLaw Graph Cuts
Algorithms based on spectral graph cut objectives such as normalized cut...
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Gamma Processes, StickBreaking, and Variational Inference
While most Bayesian nonparametric models in machine learning have focuse...
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Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichle...
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MADBayes: MAPbased Asymptotic Derivations from Bayes
The classical mixture of Gaussians model is related to Kmeans via small...
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Revisiting kmeans: New Algorithms via Bayesian Nonparametrics
Bayesian models offer great flexibility for clustering applicationsBa...
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Metric and Kernel Learning using a Linear Transformation
Metric and kernel learning are important in several machine learning app...
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Brian Kulis
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Assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at Boston University. And also a core member of the Division of Systems Engineering. Assistant professor in the Department of Computer Science and Engineering and the Department of Statistics at Ohio State University from 20122015.