
KernelNet: A DataDependent Kernel Parameterization for Deep Generative Modeling
Learning with kernels is an often resorted tool in modern machine learni...
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Finegrained Attention and Featuresharing Generative Adversarial Networks for Single Image SuperResolution
The traditional superresolution methods that aim to minimize the mean s...
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Adversarial Learning of a Sampler Based on an Unnormalized Distribution
We investigate adversarial learning in the case when only an unnormalize...
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Implicit Deep Latent Variable Models for Text Generation
Deep latent variable models (LVM) such as variational autoencoder (VAE)...
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Learning Diverse Stochastic HumanAction Generators by Learning Smooth Latent Transitions
Humanmotion generation is a longstanding challenging task due to the r...
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Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
The posteriors over neural network weights are high dimensional and mult...
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Scalable Thompson Sampling via Optimal Transport
Thompson sampling (TS) is a class of algorithms for sequential decision...
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Learning Structural Weight Uncertainty for Sequential DecisionMaking
Learning probability distributions on the weights of neural networks (NN...
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Particle Optimization in Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has been increasi...
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ZeroShot Learning via ClassConditioned Deep Generative Models
We present a deep generative model for learning to predict classes not s...
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A Convergence Analysis for A Class of Practical VarianceReduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SGMCMC) has been develope...
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ContinuousTime Flows for Deep Generative Models
Normalizing flows have been developed recently as a method for drawing s...
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ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the nonidentifiability issues associated with bidirectio...
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Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
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On the Convergence of Stochastic Gradient MCMC Algorithms with HighOrder Integrators
Recent advances in Bayesian learning with largescale data have witnesse...
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Stochastic Gradient MCMC with Stale Gradients
Stochastic gradient MCMC (SGMCMC) has played an important role in large...
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Nonparametric Bayesian Topic Modelling with the Hierarchical PitmanYor Processes
The Dirichlet process and its extension, the PitmanYor process, are sto...
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Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel fr...
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Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
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Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods are Bayes...
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Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
Effective training of deep neural networks suffers from two main issues....
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HighOrder Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Learning in deep models using Bayesian methods has generated significant...
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Scalable Bayesian NonNegative Tensor Factorization for Massive Count Data
We present a Bayesian nonnegative tensor factorization model for count...
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Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling
We develop dependent hierarchical normalized random measures and apply t...
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Theory of Dependent Hierarchical Normalized Random Measures
This paper presents theory for Normalized Random Measures (NRMs), Normal...
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Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Recurrent neural networks (RNNs) have shown promising performance for la...
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On Connecting Stochastic Gradient MCMC and Differential Privacy
Significant success has been realized recently on applying machine learn...
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A Unified ParticleOptimization Framework for Scalable Bayesian Sampling
There has been recent interest in developing scalable Bayesian sampling ...
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Policy Optimization as Wasserstein Gradient Flows
Policy optimization is a core component of reinforcement learning (RL), ...
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Stochastic ParticleOptimization Sampling and the NonAsymptotic Convergence Theory
Particleoptimization sampling (POS) is a recently developed technique t...
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Distributionally Adversarial Attack
Recent work on adversarial attack has shown that Projected Gradient Desc...
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Is PGDAdversarial Training Necessary? Alternative Training via a SoftQuantization Network with NoisyNatural Samples Only
Recent work on adversarial attack and defense suggests that PGD is a uni...
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SecondOrder Adversarial Attack and Certifiable Robustness
We propose a powerful secondorder attack method that outperforms existi...
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Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
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Variance Reduction in Stochastic ParticleOptimization Sampling
Stochastic particleoptimization sampling (SPOS) is a recentlydeveloped...
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SelfAdversarially Learned Bayesian Sampling
Scalable Bayesian sampling is playing an important role in modern machin...
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Learning Saliency Maps for Adversarial PointCloud Generation
3D pointcloud recognition with deep neural network (DNN) has received r...
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Improving SequencetoSequence Learning via Optimal Transport
Sequencetosequence models are commonly trained via maximum likelihood ...
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TopicGuided Variational Autoencoders for Text Generation
We propose a topicguided variational autoencoder (TGVAE) model for text...
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On NormAgnostic Robustness of Adversarial Training
Adversarial examples are carefully perturbed inputs for fooling machine...
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Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
Domain adaptation is an important technique to alleviate performance deg...
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Document Hashing with MixturePrior Generative Models
Hashing is promising for largescale information retrieval tasks thanks ...
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NestedWasserstein SelfImitation Learning for Sequence Generation
Reinforcement learning (RL) has been widely studied for improving sequen...
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Changyou Chen
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Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York