您正在使用IE低版浏览器,为了您的FUTUREAI账号安全和更好的产品体验,强烈建议使用更快更安全的浏览器
FUTUREAI 开发者
发私信给FUTUREAI
发送

关于吴恩达大佬的介绍

本文作者:FUTUREAI 2018-06-11 17:40
导语:吴恩达1976年出生于 伦敦 ,父亲是一位香港医生 [3] ,英文名叫Andrew Ng,吴恩达年轻时候在香港和新加坡度过。1992年吴恩达就读新加坡 莱佛士书院 ,并于1997年获得了 卡内基梅隆大学

吴恩达1976年出生于伦敦,父亲是一位香港医生 [3]  ,英文名叫Andrew Ng,吴恩达年轻时候在香港和新加坡度过。1992年吴恩达就读新加坡莱佛士书院,并于1997年获得了卡内基梅隆大学的计算机科学学士学位。之后他在1998年获得了麻省理工学院的硕士学位,并于2002年获得了加州大学伯克利分校的博士学位,并从这年开始在斯坦福大学工作。他(2002年)住在加利福尼亚州的帕洛阿尔托。

吴恩达是斯坦福大学计算机科学系和电子工程系副教授,人工智能实验室主任。吴恩达主要成就在机器学习和人工智能领域,他是人工智能和机器学习领域最权威的学者之一。

2010年,时任斯坦福大学教授的吴恩达加入谷歌开发团队XLab——这个团队已先后为谷歌开发无人驾驶汽车和谷歌眼镜两个知名项目。

吴恩达与谷歌顶级工程师开始合作建立全球最大的“神经网络”,这个神经网络能以与人类大脑学习新事物相同的方式来学习现实生活。谷歌将这个项目命名为“谷歌大脑”。

吴恩达最知名的是,所开发的人工神经网络通过观看一周YouTube视频,自主学会识别哪些是关于猫的视频。这个案例为人工智能领域翻开崭新一页。吴恩达表示,未来将会在谷歌无人驾驶汽车上使用该项技术,来识别车前面的动物或者小孩,从而及时躲避。

2014年5月16日,百度宣布吴恩达加入百度,担任百度公司首席科学家,负责百度研究院的领导工作,尤其是Baidu Brain计划。 [1] 

2014年5月19日,百度宣布任命吴恩达博士为百度首席科学家,全面负责百度研究院。这是中国互联网公司迄今为止引进的最重量级人物。消息一经公布,就成为国际科技界的关注话题。美国权威杂志《麻省理工科技评论》(MIT Technology Review)甚至用充满激情的笔调对未来给予展望:“百度将领导一个创新的软件技术时代,更加了解世界。

学术著作

Deep Learning with COTS HPC Systems

Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro and Andrew Y. Ng in ICML 2013.

Parsing with Compositional Vector Grammars

John Bauer,Richard Socher, Christopher D. Manning, Andrew Y. Ng in ACL 2013.

Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Danqi Chen,Richard Socher, Christopher D. Manning, Andrew Y. Ng in ICLR 2013.

Convolutional-Recursive Deep Learning for 3D Object Classification.

Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning, Andrew Y. Ng in NIPS 2012.

Improving Word Representations via Global Context and Multiple Word Prototypes

Eric H. Huang, Richard Socher, Christopher D. Manning and Andrew Y. Ng in ACL 2012.

Large Scale Distributed Deep Networks.

J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M.A. Ranzato, A. Senior, P. Tucker, K. Yang, A. Y. Ng in NIPS 2012.

Recurrent Neural Networks for Noise Reduction in Robust ASR.

A.L. Maas, Q.V. Le, T.M. O'Neil, O. Vinyals, P. Nguyen, and Andrew Y. Ng in Interspeech 2012.

Word-level Acoustic Modeling with Convolutional Vector Regression Learning Workshop

Andrew L. Maas, Stephen D. Miller, Tyler M. O'Neil, Andrew Y. Ng, and Patrick Nguyen in ICML 2012.

Emergence of Object-Selective Features in Unsupervised Feature Learning.

Adam Coates, Andrej Karpathy, and Andrew Y. Ng in NIPS 2012.

Deep Learning of Invariant Features via Simulated Fixations in Video

Will Y. Zou, Shenghuo Zhu, Andrew Y. Ng, Kai Yu in NIPS 2012.

Learning Feature Representations with K-means.

Adam Coates and Andrew Y. Ng in Neural Networks: Tricks of the Trade, Reloaded, Springer LNCS 2012.

Building High-Level Features using Large Scale Unsupervised Learning

Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean and Andrew Y. Ng in ICML 2012.

Semantic Compositionality through Recursive Matrix-Vector Spaces

Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng in EMNLP 2012.

End-to-End Text Recognition with Convolutional Neural Networks

Tao Wang, David J. Wu, Adam Coates and Andrew Y. Ng in ICPR 2012.

Selecting Receptive Fields in Deep Networks

Adam Coates and Andrew Y. Ng in NIPS 2011.

ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning

Quoc V. Le, Alex Karpenko, Jiquan Ngiam and Andrew Y. Ng in NIPS 2011.

Sparse Filtering

Jiquan Ngiam, Pangwei Koh, Zhenghao Chen, Sonia Bhaskar and Andrew Y. Ng in NIPS 2011.

Unsupervised Learning Models of Primary Cortical Receptive Fields and Receptive Field Plasticity

Andrew Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh and Andrew Y. Ng in NIPS 2011.

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning in NIPS 2011.

Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Y. Ng, and Christopher D. Manning in EMNLP 2011.

Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning

Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David Wu and Andrew Y. Ng in ICDAR 2011.

Parsing Natural Scenes and Natural Language with Recursive Neural Networks

Richard Socher, Cliff Lin, Andrew Y. Ng and Christopher Manning in ICML 2011.

The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization

Adam Coates and Andrew Y. Ng in ICML 2011.

On Optimization Methods for Deep Learning

Quoc V. Le, Jiquan Ngiam, Adam Coates, Abhik Lahiri, Bobby Prochnow and Andrew Y. Ng in ICML 2011.

Learning Deep Energy Models

Jiquan Ngiam, Zhenghao Chen, Pangwei Koh and Andrew Y. Ng in ICML 2011.

Multimodal Deep Learning

Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Y. Ng in ICML 2011.

On Random Weights and Unsupervised Feature Learning

Andrew Saxe, Pangwei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh and Andrew Y. Ng in ICML 2011.

Learning Hierarchical Spatio-Temporal Features for Action Recognition with Independent Subspace Analysis

Quoc V. Le, Will Zou, Serena Yeung and Andrew Y. Ng in CVPR 2011.

An Analysis of Single-Layer Networks in Unsupervised Feature Learning

Adam Coates, Honglak Lee and Andrew Ng in AISTATS 14, 2011.

Learning Word Vectors for Sentiment Analysis

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts in ACL 2011.

A Low-cost Compliant 7-DOF Robotic Manipulator

Morgan Quigley, Alan Asbeck and Andrew Y. Ng in ICRA 2011.

Grasping with Application to an Autonomous Checkout Robot

Ellen Klingbeil, Deepak Drao, Blake Carpenter, Varun Ganapathi, Oussama Khatib, Andrew Y. Ng in ICRA 2011.

Autonomous Sign Reading for Semantic Mapping

Carl Case, Bipin Suresh, Adam Coates and Andrew Y. Ng in ICRA 2011.

Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks

Richard Socher, Christopher Manning and Andrew Ng in NIPS 2010.

A Probabilistic Model for Semantic Word Vectors

Andrew Maas and Andrew Ng in NIPS 2010.

Tiled Convolutional Neural Networks

Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pangwei Koh and Andrew Y. Ng in NIPS 2010.

Energy Disaggregation via Discriminative Sparse Coding

J. Zico Kolter and Andrew Y. Ng in NIPS 2010.

Autonomous Helicopter Aerobatics through Apprenticeship Learning

Pieter Abbeel, Adam Coates and Andrew Y. Ng in IJRR 2010.

Autonomous Operation of Novel Elevators for Robot Navigation

Ellen Klingbeil, Blake Carpenter, Olga Russakovsky and Andrew Y. Ng in ICRA 2010.

Learning to Grasp Objects with Multiple Contact Points

Quoc Le, David Kamm and Andrew Y. Ng in ICRA 2010.

Multi-Camera Object Detection for Robotics

Adam Coates and Andrew Y. Ng in ICRA 2010.

A Probabilistic Approach to Mixed Open-loop and Closed-loop Control, with Application to Extreme Autonomous Driving

J. Zico Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng and Sebastian Thrun in ICRA 2010.

Grasping Novel Objects with Depth Segmentation

Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsand and Andrew Y. Ng in IROS 2010.

Low-cost Accelerometers for Robotic Manipulator Perception

Morgan Quigley, Reuben Brewer, Sai P. Soundararaj, Vijay Pradeep, Quoc V. Le and Andrew Y. Ng in IROS 2010.

A Steiner Tree Approach to Object Detection

Olga Russakovsky and Andrew Y. Ng in CVPR 2010.

Measuring Invariances in Deep Networks

Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee and Andrew Y. Ng in NIPS 2009.

Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks

Honglak Lee, Yan Largman, Peter Pham and Andrew Y. Ng in NIPS 2009.

Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations

Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng in ICML 2009.

Large-scale Deep Unsupervised Learning using Graphics Processors

Rajat Raina, Anand Madhavan and Andrew Y. Ng in ICML 2009.

A majorization-minimization algorithm for (multiple) hyperparameter learning

Chuan Sheng Foo, Chuong Do and Andrew Y. Ng in ICML 2009.

Regularization and Feature Selection in Least-Squares Temporal Difference Learning

J. Zico Kolter and Andrew Y. Ng in ICML 2009.

Near-Bayesian Exploration in Polynomial Time

J. Zico Kolter and Andrew Y. Ng in ICML 2009.

Policy Search via the Signed Derivative

J. Zico Kolter and Andrew Y. Ng in RSS 2009.

Joint Calibration of Multiple Sensors

Quoc Le and Andrew Y. Ng in IROS 2009.

Scalable Learning for Object Detection with GPU Hardware

Adam Coates, Paul Baumstarck, Quoc Le, and Andrew Y. Ng in IROS 2009.

Exponential Family Sparse Coding with Application to Self-taught Learning

Honglak Lee, Rajat Raina, Alex Teichman and Andrew Y. Ng in IJCAI 2009.

Apprenticeship Learning for Helicopter Control

Adam Coates, Pieter Abbeel and Andrew Y. Ng in Communications of the ACM, Volume 52, 2009.

ROS: An Open-Source Robot Operating System

Morgan Quigley, Brian Gerkey, Ken Conley, Josh Faust, Tully Foote, Jeremy Leibs, Eric Berger, Rob Wheeler, and Andrew Y. Ng in ICRA 2009.

High-Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening

Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc Le, Ashley Wellman and Andrew Y. Ng in ICRA 2009.

Stereo Vision and Terrain Modeling for Quadruped Robots

J. Zico Kolter, Youngjun Kim and Andrew Y. Ng in ICRA 2009.

Task-Space Trajectories via Cubic Spline Optimization

J. Zico Kolter and Andrew Y. Ng in ICRA 2009.

Learning Sound Location from a Single Microphone

Ashutosh Saxena and Andrew Y. Ng in ICRA 2009.

Learning 3-D Object Orientation from Images

Ashutosh Saxena, Justin Driemeyer and Andrew Y. Ng in ICRA 2009.

Reactive Grasping Using Optical Proximity Sensors

Kaijen Hsiao, Paul Nangeroni, Manfred Huber, Ashutosh Saxena and Andrew Y. Ng in ICRA 2009


分享:
相关文章
最新文章