Honglak Lee
E753100
Honglak Lee is a computer scientist and researcher known for his contributions to deep learning and representation learning, particularly in unsupervised feature learning.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Honglak Lee canonical | 1 |
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence researcher
ⓘ
person ⓘ |
| academicDegree | PhD in computer science ⓘ |
| citizenship | South Korea NERFINISHED ⓘ |
| countryOfCitizenship | South Korea ⓘ |
| doctoralAdvisor | Andrew Ng NERFINISHED ⓘ |
| educatedAt |
Stanford University
ⓘ
University of Michigan ⓘ |
| employer |
Google
ⓘ
LG AI Research NERFINISHED ⓘ University of Michigan NERFINISHED ⓘ |
| fieldOfWork |
computer science
ⓘ
computer vision ⓘ deep learning ⓘ machine learning ⓘ neural networks ⓘ representation learning ⓘ speech recognition ⓘ unsupervised feature learning ⓘ unsupervised learning ⓘ |
| knownFor |
convolutional deep belief networks
ⓘ
deep learning research ⓘ energy-based models for representation learning ⓘ representation learning research ⓘ sparse coding for feature learning ⓘ stacked autoencoders ⓘ unsupervised feature learning methods ⓘ |
| language |
English
ⓘ
Korean ⓘ |
| notableWork |
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations
NERFINISHED
ⓘ
Efficient Sparse Coding Algorithms NERFINISHED ⓘ Learning Hierarchical Feature Representations with Deep Networks NERFINISHED ⓘ Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks NERFINISHED ⓘ |
| positionHeld |
associate professor of computer science and engineering at the University of Michigan
ⓘ
chief scientist at LG AI Research ⓘ research scientist at Google ⓘ |
| researchInterest |
generative models
ⓘ
large-scale optimization for deep learning ⓘ semi-supervised learning ⓘ structured prediction ⓘ transfer learning ⓘ unsupervised learning ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.