D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 74 Citations 40,768 163 World Ranking 621 National Ranking 381

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

Honglak Lee mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Unsupervised learning. His study brings together the fields of Computer vision and Artificial intelligence. His biological study spans a wide range of topics, including Visual Word, Training set and Automatic image annotation.

Honglak Lee combines subjects such as Object and Boltzmann machine with his study of Pattern recognition. His research integrates issues of Classifier, Speech recognition, Representation and Robustness in his study of Deep learning. His studies in Unsupervised learning integrate themes in fields like Semi-supervised learning and Feature extraction.

His most cited work include:

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2006 citations)
  • Efficient sparse coding algorithms (1996 citations)
  • Multimodal Deep Learning (1927 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Reinforcement learning and Artificial neural network. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Computer vision. His Machine learning research incorporates elements of Pixel and Representation.

His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification and Inference. His studies deal with areas such as Recurrent neural network, Generalization, Mathematical optimization and Control theory as well as Reinforcement learning. His Deep learning research includes elements of Unsupervised learning, Generative model and Robustness.

He most often published in these fields:

  • Artificial intelligence (81.75%)
  • Machine learning (30.42%)
  • Pattern recognition (25.48%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (81.75%)
  • Reinforcement learning (23.95%)
  • Machine learning (30.42%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Machine learning, Artificial neural network and Algorithm. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition. He interconnects Smoothing, Autoencoder, Deep learning and Contextual image classification in the investigation of issues within Pattern recognition.

His Reinforcement learning research includes themes of Generalization, Mathematical optimization and Inference. His Machine learning study combines topics in areas such as Adversarial system, Pixel and Generative grammar. His Artificial neural network research incorporates themes from Flow, Labeled data, Leverage and Word error rate.

Between 2019 and 2021, his most popular works were:

  • Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks (90 citations)
  • Consistency Regularization for Generative Adversarial Networks (57 citations)
  • Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning (35 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His primary areas of study are Artificial intelligence, Artificial neural network, Generalization, Reinforcement learning and Regularization. As part of his studies on Artificial intelligence, Honglak Lee often connects relevant subjects like Machine learning. As part of one scientific family, Honglak Lee deals mainly with the area of Machine learning, narrowing it down to issues related to the Generative grammar, and often Interpretability and Feature learning.

His work deals with themes such as Radiology, Deep learning, Convolutional neural network and Encoding, which intersect with Artificial neural network. The Reinforcement learning study combines topics in areas such as Stability, Supervised learning, Flow and Manifold. His Regularization research integrates issues from Robotics and Inference.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Efficient sparse coding algorithms

Honglak Lee;Alexis Battle;Rajat Raina;Andrew Y. Ng.
neural information processing systems (2006)

3027 Citations

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng.
international conference on machine learning (2009)

2676 Citations

Multimodal Deep Learning

Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam.
international conference on machine learning (2011)

2276 Citations

An analysis of single-layer networks in unsupervised feature learning

Adam Coates;Andrew Y. Ng;Honglak Lee.
international conference on artificial intelligence and statistics (2011)

2236 Citations

Self-taught learning: transfer learning from unlabeled data

Rajat Raina;Alexis Battle;Honglak Lee;Benjamin Packer.
international conference on machine learning (2007)

1633 Citations

Learning structured output representation using deep conditional generative models

Kihyuk Sohn;Xinchen Yan;Honglak Lee.
neural information processing systems (2015)

1254 Citations

Unsupervised feature learning for audio classification using convolutional deep belief networks

Honglak Lee;Peter Pham;Yan Largman;Andrew Y. Ng.
neural information processing systems (2009)

1209 Citations

Sparse deep belief net model for visual area V2

Honglak Lee;Chaitanya Ekanadham;Andrew Y. Ng.
neural information processing systems (2007)

1172 Citations

Deep learning for detecting robotic grasps

Ian Lenz;Honglak Lee;Ashutosh Saxena.
The International Journal of Robotics Research (2015)

1039 Citations

Generative Adversarial Text to Image Synthesis

Scott Reed;Zeynep Akata;Xinchen Yan;Lajanugen Logeswaran.
arXiv: Neural and Evolutionary Computing (2016)

1036 Citations

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