
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Entanglement is a physical phenomenon, which has fueled recent successes...
read it

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in CryoElectron Tomography
Motivation: CryoElectron Tomography (cryoET) is a 3D bioimaging tool t...
read it

On Data Efficiency of Metalearning
Metalearning has enabled learning statistical models that can be quickl...
read it

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, w...
read it

XRayGAN: Consistencypreserving Generation of Xray Images from Radiology Reports
To effectively train medical students to become qualified radiologists, ...
read it

On the Generation of Medical Dialogues for COVID19
Under the pandemic of COVID19, people experiencing COVID19related symp...
read it

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...
read it

Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of huma...
read it

PathVQA: 30000+ Questions for Medical Visual Question Answering
Is it possible to develop an "AI Pathologist" to pass the boardcertifie...
read it

Generalized Zeroshot ICD Coding
The International Classification of Diseases (ICD) is a list of classifi...
read it

Efficient Exploration via State Marginal Matching
To solve tasks with sparse rewards, reinforcement learning algorithms mu...
read it

Regularizing Blackbox Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on design...
read it

SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
read it

Explaining a blackbox using Deep Variational Information Bottleneck Approach
Briefness and comprehensiveness are necessary in order to give a lot of ...
read it

Regularizing Blackbox Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
read it

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensivetoquery function is a common task in science an...
read it

Toward Unsupervised Text Content Manipulation
Controlled generation of text is of high practical use. Recent efforts h...
read it

Text Infilling
Recent years have seen remarkable progress of text generation in differe...
read it

Connecting the Dots Between MLE and RL for Sequence Generation
Sequence generation models such as recurrent networks can be trained wit...
read it

Unsupervised PseudoLabeling for Extractive Summarization on Electronic Health Records
Extractive summarization is very useful for physicians to better manage ...
read it

On the Complexity of Exploration in GoalDriven Navigation
Building agents that can explore their environments intelligently is a c...
read it

Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD1...
read it

AutoLoss: Learning Discrete Schedules for Alternate Optimization
Many machine learning problems involve iteratively and alternately optim...
read it

Missing Value Imputation Based on Deep Generative Models
Missing values widely exist in many realworld datasets, which hinders t...
read it

CIRL: Controllable Imitative Reinforcement Learning for Visionbased Selfdriving
Autonomous urban driving navigation with complex multiagent dynamics is...
read it

Geometric Generalization Based ZeroShot Learning Dataset Infinite World: Simple Yet Powerful
Raven's Progressive Matrices are one of the widely used tests in evaluat...
read it

Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable a...
read it

Gated Path Planning Networks
Value Iteration Networks (VINs) are effective differentiable path planni...
read it

Dynamicstructured Semantic Propagation Network
Semantic concept hierarchy is still underexplored for semantic segmenta...
read it

Deep learning based supervised semantic segmentation of Electron CryoSubtomograms
Cellular Electron CryoTomography (CECT) is a powerful imaging technique...
read it

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optim...
read it

Unsupervised RealtoVirtual Domain Unification for EndtoEnd Highway Driving
In the spectrum of visionbased autonomous driving, vanilla endtoend m...
read it

Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Objective Develop an automatic diagnostic system which only uses textual...
read it

On the Automatic Generation of Medical Imaging Reports
Medical imaging is widely used in clinical practice for diagnosis and tr...
read it

Predicting Discharge Medications at Admission Time Based on Deep Learning
Predicting discharge medications right after a patient being admitted is...
read it

Recurrent Estimation of Distributions
This paper presents the recurrent estimation of distributions (RED) for ...
read it

Nonparametric Variational Autoencoders for Hierarchical Representation Learning
The recently developed variational autoencoders (VAEs) have proved to be...
read it

PostInference Prior Swapping
While Bayesian methods are praised for their ability to incorporate usef...
read it

Harnessing Deep Neural Networks with Logic Rules
Combining deep neural networks with structured logic rules is desirable ...
read it

Latent Variable Modeling with DiversityInducing Mutual Angular Regularization
Latent Variable Models (LVMs) are a large family of machine learning mod...
read it

Poseidon: A System Architecture for Efficient GPUbased Deep Learning on Multiple Machines
Deep learning (DL) has achieved notable successes in many machine learni...
read it

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
We present a scalable Gaussian process model for identifying and charact...
read it

Embarrassingly Parallel Variational Inference in Nonconjugate Models
We develop a parallel variational inference (VI) procedure for use in da...
read it

Cauchy Principal Component Analysis
Principal Component Analysis (PCA) has wide applications in machine lear...
read it

Fast Function to Function Regression
We analyze the problem of regression when both input covariates and outp...
read it

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range o...
read it

Asymptotically Exact, Embarrassingly Parallel MCMC
Communication costs, resulting from synchronization requirements during ...
read it

Group Sparse Additive Models
We consider the problem of sparse variable selection in nonparametric ad...
read it
Eric Xing
is this you? claim profile
Founder and CEO, Chief Scientist at Petuum, Inc., Professor at Carnegie Mellon University