
On Leveraging Pretrained GANs for LimitedData Generation
Recent work has shown GANs can generate highly realistic images that are...
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CLUB: A Contrastive Logratio Upper Bound of Mutual Information
Mutual information (MI) minimization has gained considerable interests i...
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Toward Automatic Threat Recognition for Airport Xray Baggage Screening with Deep Convolutional Object Detection
For the safety of the traveling public, the Transportation Security Admi...
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Generative Adversarial Network Training is a Continual Learning Problem
Generative Adversarial Networks (GANs) have proven to be a powerful fram...
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Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Variational autoencoders (VAEs) with an autoregressive decoder have bee...
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Enhancing Crosstask BlackBox Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversar...
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Towards Understanding Fast Adversarial Training
Current neuralnetworkbased classifiers are susceptible to adversarial ...
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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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GromovWasserstein Learning for Graph Matching and Node Embedding
A novel GromovWasserstein learning framework is proposed to jointly mat...
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A DeepLearning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
We consider thyroidmalignancy prediction from ultrahighresolution who...
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Survival Function Matching for Calibrated TimetoEvent Predictions
Models for predicting the time of a future event are crucial for risk as...
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Graph Optimal Transport for CrossDomain Alignment
Crossdomain alignment between two sets of entities (e.g., objects in an...
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GO Gradient for ExpectationBased Objectives
Within many machine learning algorithms, a fundamental problem concerns ...
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StraightThrough Estimator as Projected Wasserstein Gradient Flow
The StraightThrough (ST) estimator is a widely used technique for back...
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Improving Disentangled Text Representation Learning with InformationTheoretic Guidance
Learning disentangled representations of natural language is essential f...
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Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
We consider preoperative prediction of thyroid cancer based on ultrahig...
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Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain...
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Towards Amortized RankingCritical Training for Collaborative Filtering
Collaborative filtering is widely used in modern recommender systems. Re...
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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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Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting t...
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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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LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future fram...
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Towards Learning a Generic Agent for VisionandLanguage Navigation via Pretraining
Learning to navigate in a visual environment following naturallanguage ...
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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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Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
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Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on th...
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Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network (ΔGAN) is developed for semi...
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A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
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Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on th...
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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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An innerloop free solution to inverse problems using deep neural networks
We propose a new method that uses deep learning techniques to accelerate...
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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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Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
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Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
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Compressive Sensing via Convolutional Factor Analysis
We solve the compressive sensing problem via convolutional factor analys...
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Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
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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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Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as...
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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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Factored Temporal Sigmoid Belief Networks for Sequence Learning
Deep conditional generative models are developed to simultaneously learn...
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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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Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen tial observations can...
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Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential depende...
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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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ZeroTruncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for lowrank factorization of massi...
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