
Flow++: Improving FlowBased Generative Models with Variational Dequantization and Architecture Design
Flowbased generative models are powerful exact likelihood models with e...
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Learning Visual Relation Priors for ImageText Matching and Image Captioning with Neural Scene Graph Generators
Grounding language to visual relations is critical to various languagea...
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Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
Datadriven sequential decision has found a wide range of applications i...
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BoundaryAware Network for Fast and HighAccuracy Portrait Segmentation
Compared with other semantic segmentation tasks, portrait segmentation r...
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A Fully Online Approach for Covariance Matrices Estimation of Stochastic Gradient Descent Solutions
Stochastic gradient descent (SGD) algorithm is widely used for parameter...
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Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Quantum autoencoders allow for reducing the amount of resources in a qua...
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Learning from Demonstration in the Wild
Learning from demonstration (LfD) is useful in settings where handcodin...
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Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Recent advances in deep reinforcement learning algorithms have shown gre...
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Adaptive Discrete Smoothing for HighDimensional and Nonlinear Panel Data
In this paper we develop a datadriven smoothing technique for highdime...
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Resolution Adaptive Networks for Efficient Inference
Recently, adaptive inference is gaining increasing attention due to its ...
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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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Evaluating Protein Transfer Learning with TAPE
Protein modeling is an increasingly popular area of machine learning res...
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Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited b...
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Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
A key challenge in leveraging data augmentation for neural network train...
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On the Fairness of Randomized Trials for Recommendation With Heterogeneous Demographics and Beyond
Observed events in recommendation are consequence of the decisions made ...
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ALPINE: Active Link Prediction using Network Embedding
Many realworld problems can be formalized as predicting links in a part...
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Dimension Independent Generalization Error with Regularized Online Optimization
One classical canon of statistics is that large models are prone to over...
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HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for humanp...
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Deep InceptionResidual Laplacian Pyramid Networks for Accurate Single Image SuperResolution
With exploiting contextual information over large image regions in an ef...
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Constructing multimodality and multiclassifier radiomics predictive models through reliable classifier fusion
Radiomics aims to extract and analyze large numbers of quantitative feat...
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A Note on Tight Lower Bound for MNLBandit Assortment Selection Models
In this note we prove a tight lower bound for the MNLbandit assortment ...
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Nonstationary Stochastic Optimization with Local Spatial and Temporal Changes
We consider a nonstationary sequential stochastic optimization problem,...
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An Effective Training Method For Deep Convolutional Neural Network
In this paper, we propose the nonlinearity generation method to speed up...
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A Nearly Instance Optimal Algorithm for Topk Ranking under the Multinomial Logit Model
We study the active learning problem of topk ranking from multiwise co...
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MetaLearning with Temporal Convolutions
Deep neural networks excel in regimes with large amounts of data, but te...
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Parameter Space Noise for Exploration
Deep reinforcement learning (RL) methods generally engage in exploratory...
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#Exploration: A Study of CountBased Exploration for Deep Reinforcement Learning
Countbased exploration algorithms are known to perform nearoptimally w...
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We explore the use of Evolution Strategies (ES), a class of black box op...
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PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
PixelCNNs are a recently proposed class of powerful generative models wi...
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Bayesian Decision Process for CostEfficient Dynamic Ranking via Crowdsourcing
Rank aggregation based on pairwise comparisons over a set of items has a...
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RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning so...
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Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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Statistical Inference for Model Parameters in Stochastic Gradient Descent
The stochastic gradient descent (SGD) algorithm has been widely used in ...
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Wasserstein Distributional Robustness and Regularization in Statistical Learning
A central question in statistical learning is to design algorithms that ...
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Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an informationtheoretic extension to the ...
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VIME: Variational Information Maximizing Exploration
Scalable and effective exploration remains a key challenge in reinforcem...
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Competitive analysis of the topK ranking problem
Motivated by applications in recommender systems, web search, social cho...
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The Role of Context Selection in Object Detection
We investigate the reasons why context in object detection has limited u...
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Evaluating computational models of explanation using human judgments
We evaluate four computational models of explanation in Bayesian network...
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Improved Techniques for Training GANs
We present a variety of new architectural features and training procedur...
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Generating Discriminative Object Proposals via Submodular Ranking
A multiscale greedybased object proposal generation approach is presen...
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Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
In crowd labeling, a large amount of unlabeled data instances are outsou...
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Sequential Optimization for Efficient HighQuality Object Proposal Generation
We are motivated by the need for a generic object proposal generation al...
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Unsupervised Network Pretraining via Encoding Human Design
Over the years, computer vision researchers have spent an immense amount...
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Group Sparse Additive Models
We consider the problem of sparse variable selection in nonparametric ad...
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Smoothing Proximal Gradient Method for General Structured Sparse Learning
We study the problem of learning high dimensional regression models regu...
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Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
The gesture recognition using motion capture data and depth sensors has ...
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Safer Classification by Synthesis
The discriminative approach to classification using deep neural networks...
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Hierarchical Spatial Transformer Network
Computer vision researchers have been expecting that neural networks hav...
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Xi Chen
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The University of Texas at Arlington
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Software Engineer at Microsoft