
Integrating Semantics and Neighborhood Information with GraphDriven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, docume...
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Unsupervised Hashing with Contrastive Information Bottleneck
Many unsupervised hashing methods are implicitly established on the idea...
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Learning HighDimensional Distributions with Latent Neural FokkerPlanck Kernels
Learning highdimensional distributions is an important yet challenging ...
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Towards Fair Federated Learning with ZeroShot Data Augmentation
Federated learning has emerged as an important distributed learning para...
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MetaLearning with Neural Tangent Kernels
Model Agnostic MetaLearning (MAML) has emerged as a standard framework ...
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Transformerbased Conditional Variational Autoencoder for Controllable Story Generation
We investigate largescale latent variable models (LVMs) for neural stor...
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Outline to Story: Finegrained Controllable Story Generation from Cascaded Events
Largescale pretrained language models have shown thrilling generation c...
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What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure
In recent times, BERT based transformer models have become an inseparabl...
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SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks i...
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My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism
Significant progress has been made in deeplearning based Automatic Essa...
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ReMP: Rectified Metric Propagation for FewShot Learning
Fewshot learning features the capability of generalizing from a few exa...
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Unpaired ImagetoImage Translation via Latent Energy Transport
Imagetoimage translation aims to preserve source contents while transl...
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MixKD: Towards Efficient Distillation of Largescale Language Models
Largescale language models have recently demonstrated impressive empiri...
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Learning Manifold Implicitly via Explicit HeatKernel Learning
Manifold learning is a fundamental problem in machine learning with nume...
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Repulsive Attention: Rethinking Multihead Attention as Bayesian Inference
The neural attention mechanism plays an important role in many natural l...
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StructureAware HumanAction Generation
Generating longrange skeletonbased human actions has been a challengin...
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Generative Semantic Hashing Enhanced via Boltzmann Machines
Generative semantic hashing is a promising technique for largescale inf...
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Graph Neural Networks with Composite Kernels
Learning on graph structured data has drawn increasing interest in recen...
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Towards Understanding the Adversarial Vulnerability of Skeletonbased Action Recognition
Skeletonbased action recognition has attracted increasing attention due...
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Reward Constrained Interactive Recommendation with Natural Language Feedback
Textbased interactive recommendation provides richer user feedback and ...
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Improving Adversarial Text Generation by Modeling the Distant Future
Autoregressive text generation models usually focus on local fluency, a...
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Towards Faithful Neural TabletoText Generation with ContentMatching Constraints
Text generation from a knowledge base aims to translate knowledge triple...
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Discretized Bottleneck in VAE: PosteriorCollapseFree SequencetoSequence Learning
Variational autoencoders (VAEs) are important tools in endtoend repres...
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Decomposed Adversarial Learned Inference
Effective inference for a generative adversarial model remains an import...
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Feature Quantization Improves GAN Training
The instability in GAN training has been a longstanding problem despite...
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NestedWasserstein SelfImitation Learning for Sequence Generation
Reinforcement learning (RL) has been widely studied for improving sequen...
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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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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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Implicit Deep Latent Variable Models for Text Generation
Deep latent variable models (LVM) such as variational autoencoder (VAE)...
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Document Hashing with MixturePrior Generative Models
Hashing is promising for largescale information retrieval tasks thanks ...
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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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On NormAgnostic Robustness of Adversarial Training
Adversarial examples are carefully perturbed inputs for fooling machine...
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TopicGuided Variational Autoencoders for Text Generation
We propose a topicguided variational autoencoder (TGVAE) model for text...
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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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Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
The posteriors over neural network weights are high dimensional and mult...
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Improving SequencetoSequence Learning via Optimal Transport
Sequencetosequence models are commonly trained via maximum likelihood ...
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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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Learning Saliency Maps for Adversarial PointCloud Generation
3D pointcloud recognition with deep neural network (DNN) has received r...
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SelfAdversarially Learned Bayesian Sampling
Scalable Bayesian sampling is playing an important role in modern machin...
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Variance Reduction in Stochastic ParticleOptimization Sampling
Stochastic particleoptimization sampling (SPOS) is a recentlydeveloped...
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Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
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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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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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Policy Optimization as Wasserstein Gradient Flows
Policy optimization is a core component of reinforcement learning (RL), ...
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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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Learning Structural Weight Uncertainty for Sequential DecisionMaking
Learning probability distributions on the weights of neural networks (NN...
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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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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