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Learning by Ignoring
Learning by ignoring, which identifies less important things and exclude...
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Learning by Self-Explanation, with Application to Neural Architecture Search
Learning by self-explanation, where students explain a learned topic to ...
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Small-Group Learning, with Application to Neural Architecture Search
Small-group learning is a broadly used methodology in human learning and...
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DSRNA: Differentiable Search of Robust Neural Architectures
In deep learning applications, the architectures of deep neural networks...
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Skillearn: Machine Learning Inspired by Humans' Learning Skills
Humans, as the most powerful learners on the planet, have accumulated a ...
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Learning by Passing Tests, with Application to Neural Architecture Search
Learning through tests is a broadly used methodology in human learning a...
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Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
While deep learning methods have shown great success in medical image an...
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TreeGAN: Incorporating Class Hierarchy into Image Generation
Conditional image generation (CIG) is a widely studied problem in comput...
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Contrastive Self-supervised Learning for Graph Classification
Graph classification is a widely studied problem and has broad applicati...
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XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
To effectively train medical students to become qualified radiologists, ...
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Differentially-private Federated Neural Architecture Search
Neural architecture search, which aims to automatically search for archi...
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CERT: Contrastive Self-supervised Learning for Language Understanding
Pretrained language models such as BERT, GPT have shown great effectiven...
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On the Generation of Medical Dialogues for COVID-19
Under the pandemic of COVID-19, people experiencing COVID19-related symp...
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MedDialog: A Large-scale Medical Dialogue Dataset
Medical dialogue systems are promising in assisting in telemedicine to i...
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Identifying Radiological Findings Related to COVID-19 from Medical Literature
Coronavirus disease 2019 (COVID-19) has infected more than one million i...
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COVID-CT-Dataset: A CT Scan Dataset about COVID-19
CT scans are promising in providing accurate, fast, and cheap screening ...
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PathVQA: 30000+ Questions for Medical Visual Question Answering
Is it possible to develop an "AI Pathologist" to pass the board-certifie...
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Generalized Zero-shot ICD Coding
The International Classification of Diseases (ICD) is a list of classifi...
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Adversarial Domain Adaptation Being Aware of Class Relationships
Adversarial training is a useful approach to promote the learning of tra...
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Explaining a black-box using Deep Variational Information Bottleneck Approach
Briefness and comprehensiveness are necessary in order to give a lot of ...
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Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Extractive summarization is very useful for physicians to better manage ...
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Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
We study the problem of alleviating the instability issue in the GAN tra...
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Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-1...
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Missing Value Imputation Based on Deep Generative Models
Missing values widely exist in many real-world datasets, which hinders t...
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Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Distance metric learning (DML), which learns a distance metric from labe...
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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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Learning Less-Overlapping Representations
In representation learning (RL), how to make the learned representations...
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Stacked Kernel Network
Kernel methods are powerful tools to capture nonlinear patterns behind d...
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Diversity-Promoting Bayesian Learning of Latent Variable Models
To address three important issues involved in latent variable models (LV...
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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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Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition
Biomedical named entity recognition (NER) is a fundamental task in text ...
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Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning
A primary goal of computational phenotype research is to conduct medical...
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Towards Automated ICD Coding Using Deep Learning
International Classification of Diseases(ICD) is an authoritative health...
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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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Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
Deep learning models can take weeks to train on a single GPU-equipped ma...
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Strategies and Principles of Distributed Machine Learning on Big Data
The rise of Big Data has led to new demands for Machine Learning (ML) sy...
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Latent Variable Modeling with Diversity-Inducing 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 GPU-based Deep Learning on Multiple Machines
Deep learning (DL) has achieved notable successes in many machine learni...
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Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
We propose a distributed approach to train deep neural networks (DNNs), ...
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Cauchy Principal Component Analysis
Principal Component Analysis (PCA) has wide applications in machine lear...
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Crypto-Nets: Neural Networks over Encrypted Data
The problem we address is the following: how can a user employ a predict...
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Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advance...
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Integrating Document Clustering and Topic Modeling
Document clustering and topic modeling are two closely related tasks whi...
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