Michael C. Hughes
9 papers · 2017–2024 · 6 venues · across top CS/AI venues
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Achievements
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🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (22) 🐣 Hot Topic Early Bird
🧭
Keyword Pioneer
Top co-authors
Research topics
Machine Learning
(8)
Deep Learning
(4)
Learning Types
(4)
Artificial Intelligence
(3)
Computer Vision
(3)
Application Areas
(3)
Optimization & Theory
(3)
Domain-Specific
(2)
Techniques
(2)
Data Science & Analytics
(1)
Healthcare & Medicine
(1)
Applications
(1)
Bayesian & Probabilistic · Machine Learning
(1)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Clinical
(1)
Keywords
semi-supervised learning
(3)
aortic stenosis
(2)
bayesian nonparametrics
(1)
dirichlet process
(1)
attention mechanism
(1)
variational inference
(1)
text mining
(1)
clinical prediction
(1)
self-supervised learning
(1)
medical imaging
(1)
latent dirichlet allocation
(1)
topic modeling
(1)
medical image classification
(1)
image denoising
(1)
multi-task learning
(1)
multiple instance learning
(1)
patch-based model
(1)
gradient descent
(1)
hierarchical model
(1)
contrastive learning
(1)
Papers
Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification
CVPR 2024
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
MLHC 2023
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
AISTATS 2023
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
MLHC 2021
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
MLHC 2021
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
MLHC 2019
From Patches to Images: A Nonparametric Generative Model
ICML 2017
Refinery: An Open Source Topic Modeling Web Platform
JMLR 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
IJCAI 2017