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10/04/2022
Supervised Metric Learning for Retrieval via Contextual Similarity Optimization
Existing deep metric learning approaches fall into three general categor...
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06/15/2022
Latency Control for Keyword Spotting
Conversational agents commonly utilize keyword spotting (KWS) to initiat...
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05/26/2022
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Domain Adaptation (DA) has received widespread attention from deep learn...
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11/02/2021
Faster Convex Lipschitz Regression via 2-block ADMM
The task of approximating an arbitrary convex function arises in several...
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09/29/2021
Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting
In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural ...
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07/20/2021
β-Annealed Variational Autoencoder for glitches
Gravitational wave detectors such as LIGO and Virgo are susceptible to v...
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05/16/2021
Substitutional Neural Image Compression
We describe Substitutional Neural Image Compression (SNIC), a general ap...
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10/20/2020
Real-time Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semanti...
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07/05/2020
Piecewise Linear Regression via a Difference of Convex Functions
We present a new piecewise linear regression methodology that utilizes f...
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05/06/2020
Deep Divergence Learning
Classical linear metric learning methods have recently been extended alo...
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04/23/2020
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic s...
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08/20/2019
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Despite achieving remarkable success in various domains, recent studies ...
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05/28/2019
Learning Bregman Divergences
Metric learning is the problem of learning a task-specific distance func...
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06/26/2018
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Existing automatic music generation approaches that feature deep learnin...
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11/22/2017
W-Net: A Deep Model for Fully Unsupervised Image Segmentation
While significant attention has been recently focused on designing super...
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07/26/2017
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the...
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07/13/2017
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Methods that align distributions by minimizing an adversarial distance b...
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04/07/2016
Combinatorial Topic Models using Small-Variance Asymptotics
Topic models have emerged as fundamental tools in unsupervised machine l...
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01/10/2016
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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11/16/2014
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
We present a simple but powerful reinterpretation of kernelized locality...
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10/29/2014
Power-Law Graph Cuts
Algorithms based on spectral graph cut objectives such as normalized cut...
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10/04/2014
Gamma Processes, Stick-Breaking, and Variational Inference
While most Bayesian nonparametric models in machine learning have focuse...
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05/28/2013
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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12/10/2012
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
The classical mixture of Gaussians model is related to K-means via small...
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11/02/2011
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Bayesian models offer great flexibility for clustering applications---Ba...
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10/30/2009
