
On Leveraging Pretrained GANs for LimitedData Generation
Recent work has shown GANs can generate highly realistic images that are...
read it

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...
read it

Generative Adversarial Network Training is a Continual Learning Problem
Generative Adversarial Networks (GANs) have proven to be a powerful fram...
read it

Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Variational autoencoders (VAEs) with an autoregressive decoder have bee...
read it

Enhancing Crosstask BlackBox Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversar...
read it

Adversarial Learning of a Sampler Based on an Unnormalized Distribution
We investigate adversarial learning in the case when only an unnormalize...
read it

GromovWasserstein Learning for Graph Matching and Node Embedding
A novel GromovWasserstein learning framework is proposed to jointly mat...
read it

A DeepLearning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
We consider thyroidmalignancy prediction from ultrahighresolution who...
read it

Survival Function Matching for Calibrated TimetoEvent Predictions
Models for predicting the time of a future event are crucial for risk as...
read it

GO Gradient for ExpectationBased Objectives
Within many machine learning algorithms, a fundamental problem concerns ...
read it

StraightThrough Estimator as Projected Wasserstein Gradient Flow
The StraightThrough (ST) estimator is a widely used technique for back...
read it

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
We consider preoperative prediction of thyroid cancer based on ultrahig...
read it

Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain...
read it

Towards Amortized RankingCritical Training for Collaborative Filtering
Collaborative filtering is widely used in modern recommender systems. Re...
read it

Scalable Thompson Sampling via Optimal Transport
Thompson sampling (TS) is a class of algorithms for sequential decision...
read it

Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting t...
read it

Learning Structural Weight Uncertainty for Sequential DecisionMaking
Learning probability distributions on the weights of neural networks (NN...
read it

LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future fram...
read it

Towards Learning a Generic Agent for VisionandLanguage Navigation via Pretraining
Learning to navigate in a visual environment following naturallanguage ...
read it

ZeroShot Learning via ClassConditioned Deep Generative Models
We present a deep generative model for learning to predict classes not s...
read it

Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
read it

Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on th...
read it

Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network (ΔGAN) is developed for semi...
read it

A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
read it

Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on th...
read it

A Convergence Analysis for A Class of Practical VarianceReduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SGMCMC) has been develope...
read it

ContinuousTime Flows for Deep Generative Models
Normalizing flows have been developed recently as a method for drawing s...
read it

An innerloop free solution to inverse problems using deep neural networks
We propose a new method that uses deep learning techniques to accelerate...
read it

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the nonidentifiability issues associated with bidirectio...
read it

Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
read it

Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
read it

Compressive Sensing via Convolutional Factor Analysis
We solve the compressive sensing problem via convolutional factor analys...
read it

Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
read it

On the Convergence of Stochastic Gradient MCMC Algorithms with HighOrder Integrators
Recent advances in Bayesian learning with largescale data have witnesse...
read it

Stochastic Gradient MCMC with Stale Gradients
Stochastic gradient MCMC (SGMCMC) has played an important role in large...
read it

Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as...
read it

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel fr...
read it

Factored Temporal Sigmoid Belief Networks for Sequence Learning
Deep conditional generative models are developed to simultaneously learn...
read it

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
read it

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods are Bayes...
read it

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
Effective training of deep neural networks suffers from two main issues....
read it

HighOrder Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Learning in deep models using Bayesian methods has generated significant...
read it

Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen tial observations can...
read it

Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential depende...
read it

Scalable Bayesian NonNegative Tensor Factorization for Massive Count Data
We present a Bayesian nonnegative tensor factorization model for count...
read it

ZeroTruncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for lowrank factorization of massi...
read it

Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing an...
read it

StickBreaking Policy Learning in DecPOMDPs
Expectation maximization (EM) has recently been shown to be an efficient...
read it

NonGaussian Discriminative Factor Models via the MaxMargin RankLikelihood
We consider the problem of discriminative factor analysis for data that ...
read it

A Generative Model for Deep Convolutional Learning
A generative model is developed for deep (multilayered) convolutional d...
read it