
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, w...
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XRayGAN: Consistencypreserving Generation of Xray Images from Radiology Reports
To effectively train medical students to become qualified radiologists, ...
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On the Generation of Medical Dialogues for COVID19
Under the pandemic of COVID19, people experiencing COVID19related symp...
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Show, Describe and Conclude: On Exploiting the Structure Information of Chest XRay Reports
Chest XRay (CXR) images are commonly used for clinical screening and di...
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Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of huma...
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PathVQA: 30000+ Questions for Medical Visual Question Answering
Is it possible to develop an "AI Pathologist" to pass the boardcertifie...
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Generalized Zeroshot ICD Coding
The International Classification of Diseases (ICD) is a list of classifi...
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Efficient Exploration via State Marginal Matching
To solve tasks with sparse rewards, reinforcement learning algorithms mu...
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Regularizing Blackbox Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on design...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Explaining a blackbox using Deep Variational Information Bottleneck Approach
Briefness and comprehensiveness are necessary in order to give a lot of ...
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Regularizing Blackbox Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
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ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensivetoquery function is a common task in science an...
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Toward Unsupervised Text Content Manipulation
Controlled generation of text is of high practical use. Recent efforts h...
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Text Infilling
Recent years have seen remarkable progress of text generation in differe...
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Connecting the Dots Between MLE and RL for Sequence Generation
Sequence generation models such as recurrent networks can be trained wit...
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Unsupervised PseudoLabeling for Extractive Summarization on Electronic Health Records
Extractive summarization is very useful for physicians to better manage ...
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On the Complexity of Exploration in GoalDriven Navigation
Building agents that can explore their environments intelligently is a c...
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Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD1...
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AutoLoss: Learning Discrete Schedules for Alternate Optimization
Many machine learning problems involve iteratively and alternately optim...
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Missing Value Imputation Based on Deep Generative Models
Missing values widely exist in many realworld datasets, which hinders t...
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CIRL: Controllable Imitative Reinforcement Learning for Visionbased Selfdriving
Autonomous urban driving navigation with complex multiagent dynamics is...
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Geometric Generalization Based ZeroShot Learning Dataset Infinite World: Simple Yet Powerful
Raven's Progressive Matrices are one of the widely used tests in evaluat...
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Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable a...
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Gated Path Planning Networks
Value Iteration Networks (VINs) are effective differentiable path planni...
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Dynamicstructured Semantic Propagation Network
Semantic concept hierarchy is still underexplored for semantic segmenta...
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Deep learning based supervised semantic segmentation of Electron CryoSubtomograms
Cellular Electron CryoTomography (CECT) is a powerful imaging technique...
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Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optim...
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Unsupervised RealtoVirtual Domain Unification for EndtoEnd Highway Driving
In the spectrum of visionbased autonomous driving, vanilla endtoend m...
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Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Objective Develop an automatic diagnostic system which only uses textual...
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On the Automatic Generation of Medical Imaging Reports
Medical imaging is widely used in clinical practice for diagnosis and tr...
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Predicting Discharge Medications at Admission Time Based on Deep Learning
Predicting discharge medications right after a patient being admitted is...
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Recurrent Estimation of Distributions
This paper presents the recurrent estimation of distributions (RED) for ...
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Nonparametric Variational Autoencoders for Hierarchical Representation Learning
The recently developed variational autoencoders (VAEs) have proved to be...
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PostInference Prior Swapping
While Bayesian methods are praised for their ability to incorporate usef...
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Harnessing Deep Neural Networks with Logic Rules
Combining deep neural networks with structured logic rules is desirable ...
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Latent Variable Modeling with DiversityInducing Mutual Angular Regularization
Latent Variable Models (LVMs) are a large family of machine learning mod...
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Poseidon: A System Architecture for Efficient GPUbased Deep Learning on Multiple Machines
Deep learning (DL) has achieved notable successes in many machine learni...
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Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
We present a scalable Gaussian process model for identifying and charact...
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Embarrassingly Parallel Variational Inference in Nonconjugate Models
We develop a parallel variational inference (VI) procedure for use in da...
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Cauchy Principal Component Analysis
Principal Component Analysis (PCA) has wide applications in machine lear...
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Fast Function to Function Regression
We analyze the problem of regression when both input covariates and outp...
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Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range o...
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Asymptotically Exact, Embarrassingly Parallel MCMC
Communication costs, resulting from synchronization requirements during ...
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Group Sparse Additive Models
We consider the problem of sparse variable selection in nonparametric ad...
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Eric Xing
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Founder and CEO, Chief Scientist at Petuum, Inc., Professor at Carnegie Mellon University