
Generative Adversarial Network Training is a Continual Learning Problem
Generative Adversarial Networks (GANs) have proven to be a powerful fram...
11/27/2018 ∙ by Kevin J Liang, et al. ∙ 22 ∙ shareread it

Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Variational autoencoders (VAEs) with an autoregressive decoder have bee...
03/25/2019 ∙ by Hao Fu, et al. ∙ 22 ∙ shareread it

Enhancing Crosstask BlackBox Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversar...
11/22/2019 ∙ by Yantao Lu, et al. ∙ 21 ∙ shareread it

Adversarial Learning of a Sampler Based on an Unnormalized Distribution
We investigate adversarial learning in the case when only an unnormalize...
01/03/2019 ∙ by Chunyuan Li, et al. ∙ 14 ∙ shareread it

GromovWasserstein Learning for Graph Matching and Node Embedding
A novel GromovWasserstein learning framework is proposed to jointly mat...
01/17/2019 ∙ by Hongteng Xu, et al. ∙ 12 ∙ shareread it

A DeepLearning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
We consider thyroidmalignancy prediction from ultrahighresolution who...
04/26/2019 ∙ by David Dov, et al. ∙ 12 ∙ shareread it

Survival Function Matching for Calibrated TimetoEvent Predictions
Models for predicting the time of a future event are crucial for risk as...
05/21/2019 ∙ by Paidamoyo Chapfuwa, et al. ∙ 10 ∙ shareread it

GO Gradient for ExpectationBased Objectives
Within many machine learning algorithms, a fundamental problem concerns ...
01/17/2019 ∙ by Yulai Cong, et al. ∙ 6 ∙ shareread it

StraightThrough Estimator as Projected Wasserstein Gradient Flow
The StraightThrough (ST) estimator is a widely used technique for back...
10/05/2019 ∙ by Pengyu Cheng, et al. ∙ 5 ∙ shareread it

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
We consider preoperative prediction of thyroid cancer based on ultrahig...
03/29/2019 ∙ by David Dov, et al. ∙ 4 ∙ shareread it

Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain...
06/05/2019 ∙ by Shuyang Dai, et al. ∙ 4 ∙ shareread it

Towards Amortized RankingCritical Training for Collaborative Filtering
Collaborative filtering is widely used in modern recommender systems. Re...
06/10/2019 ∙ by Sam Lobel, et al. ∙ 3 ∙ shareread it

Scalable Thompson Sampling via Optimal Transport
Thompson sampling (TS) is a class of algorithms for sequential decision...
02/19/2019 ∙ by Ruiyi Zhang, et al. ∙ 2 ∙ shareread it

Learning Structural Weight Uncertainty for Sequential DecisionMaking
Learning probability distributions on the weights of neural networks (NN...
12/30/2017 ∙ by Ruiyi Zhang, et al. ∙ 1 ∙ shareread it

LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future fram...
06/24/2019 ∙ by Dong Wang, et al. ∙ 1 ∙ shareread it

ZeroShot Learning via ClassConditioned Deep Generative Models
We present a deep generative model for learning to predict classes not s...
11/15/2017 ∙ by Wenlin Wang, et al. ∙ 0 ∙ shareread it

Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
10/14/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ shareread it

Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on th...
10/03/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ shareread it

Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network (ΔGAN) is developed for semi...
09/19/2017 ∙ by Zhe Gan, et al. ∙ 0 ∙ shareread it

A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
09/18/2017 ∙ by Qinliang Su, et al. ∙ 0 ∙ shareread it

Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on th...
09/06/2017 ∙ by Liqun Chen, et al. ∙ 0 ∙ shareread it

A Convergence Analysis for A Class of Practical VarianceReduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SGMCMC) has been develope...
09/04/2017 ∙ by Changyou Chen, et al. ∙ 0 ∙ shareread it

ContinuousTime Flows for Deep Generative Models
Normalizing flows have been developed recently as a method for drawing s...
09/04/2017 ∙ by Changyou Chen, et al. ∙ 0 ∙ shareread it

An innerloop free solution to inverse problems using deep neural networks
We propose a new method that uses deep learning techniques to accelerate...
09/06/2017 ∙ by Qi Wei, et al. ∙ 0 ∙ shareread it

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the nonidentifiability issues associated with bidirectio...
09/05/2017 ∙ by Chunyuan Li, et al. ∙ 0 ∙ shareread it

Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
06/16/2017 ∙ by Changwei Hu, et al. ∙ 0 ∙ shareread it

Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
06/05/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ shareread it

Compressive Sensing via Convolutional Factor Analysis
We solve the compressive sensing problem via convolutional factor analys...
01/11/2017 ∙ by Xin Yuan, et al. ∙ 0 ∙ shareread it

Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
11/15/2016 ∙ by Qinliang Su, et al. ∙ 0 ∙ shareread it

On the Convergence of Stochastic Gradient MCMC Algorithms with HighOrder Integrators
Recent advances in Bayesian learning with largescale data have witnesse...
10/21/2016 ∙ by Changyou Chen, et al. ∙ 0 ∙ shareread it

Stochastic Gradient MCMC with Stale Gradients
Stochastic gradient MCMC (SGMCMC) has played an important role in large...
10/21/2016 ∙ by Changyou Chen, et al. ∙ 0 ∙ shareread it

Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as...
09/28/2016 ∙ by Yunchen Pu, et al. ∙ 0 ∙ shareread it

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel fr...
06/02/2016 ∙ by Qinliang Su, et al. ∙ 0 ∙ shareread it

Factored Temporal Sigmoid Belief Networks for Sequence Learning
Deep conditional generative models are developed to simultaneously learn...
05/22/2016 ∙ by Jiaming Song, et al. ∙ 0 ∙ shareread it

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
02/25/2016 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ shareread it

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods are Bayes...
12/25/2015 ∙ by Changyou Chen, et al. ∙ 0 ∙ shareread it

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
Effective training of deep neural networks suffers from two main issues....
12/23/2015 ∙ by Chunyuan Li, et al. ∙ 0 ∙ shareread it

HighOrder Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Learning in deep models using Bayesian methods has generated significant...
12/23/2015 ∙ by Chunyuan Li, et al. ∙ 0 ∙ shareread it

Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen tial observations can...
12/16/2015 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ shareread it

Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential depende...
09/23/2015 ∙ by Zhe Gan, et al. ∙ 0 ∙ shareread it

Scalable Bayesian NonNegative Tensor Factorization for Massive Count Data
We present a Bayesian nonnegative tensor factorization model for count...
08/18/2015 ∙ by Changwei Hu, et al. ∙ 0 ∙ shareread it

ZeroTruncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for lowrank factorization of massi...
08/18/2015 ∙ by Changwei Hu, et al. ∙ 0 ∙ shareread it

Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing an...
06/19/2015 ∙ by Shaobo Han, et al. ∙ 0 ∙ shareread it

StickBreaking Policy Learning in DecPOMDPs
Expectation maximization (EM) has recently been shown to be an efficient...
05/01/2015 ∙ by Miao Liu, et al. ∙ 0 ∙ shareread it

NonGaussian Discriminative Factor Models via the MaxMargin RankLikelihood
We consider the problem of discriminative factor analysis for data that ...
04/28/2015 ∙ by Xin Yuan, et al. ∙ 0 ∙ shareread it

A Generative Model for Deep Convolutional Learning
A generative model is developed for deep (multilayered) convolutional d...
04/15/2015 ∙ by Yunchen Pu, et al. ∙ 0 ∙ shareread it

Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise
We propose a globally convergent alternating minimization (AM) algorithm...
12/29/2014 ∙ by Yan Kaganovsky, et al. ∙ 0 ∙ shareread it

Generative Deep Deconvolutional Learning
A generative Bayesian model is developed for deep (multilayer) convolut...
12/18/2014 ∙ by Yunchen Pu, et al. ∙ 0 ∙ shareread it

Spectrally Grouped Total Variation Reconstruction for Scatter Imaging Using ADMM
We consider Xray coherent scatter imaging, where the goal is to reconst...
01/29/2016 ∙ by Ikenna Odinaka, et al. ∙ 0 ∙ shareread it

Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture Xray Coherent Scatter Imaging
In xray coherent scatter tomography, tomographic measurements of the fo...
01/29/2016 ∙ by Ikenna Odinaka, et al. ∙ 0 ∙ shareread it