吳恩達

2002年開始在斯坦福大學工作。

2010年加入谷歌開發團隊XLab——這個團隊已先後為谷歌開發無人駕駛汽車和谷歌眼鏡兩個知名項目。

2014年5月16日百度宣布吳恩達加入百度，擔任百度公司首席科學家，負責百度研究院的領導工作，尤其是Baidu Brain計劃。

2014年5月19日百度宣布任命為百度首席科學家，全面負責百度研究院。

2017年10月將出任Woebot公司新任董事長，該公司擁有一款同名聊天機器人。

妻子卡羅爾·萊利（Carol Reiley），女兒Nova。

研究領域

機器學習和人工智慧，研究重點是深度學習（Deep Learning）。

論文

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。

早期的工作包括斯坦福自動控制直升機項目，團隊開發了世界上最先進的自動控制直升機之一，同時也是機器學習、機器人技術和相關領域的100多篇論文的作者或合作者，他在計算機視覺的一些工作被一系列的出版物和評論文章所重點引用。

與谷歌頂級工程師開始合作建立全球最大的“神經網路”，這個神經網路能以與人類大腦學習新事物相同的方式來學習現實生活。谷歌將這個項目命名為“谷歌大腦”。

2011年在谷歌成立了“Google Brain”項目，這個項目利用谷歌的分散式計算框架計算和學習大規模人工神經網路。這個項目重要研究成果是，在16000個CPU核心上利用深度學習演演算法學習到的10億參數的神經網路，能夠在沒有任何先驗知識的情況下，僅僅通過觀看無標註的YouTube的視頻學習到識別高級別的概念，如貓，這就是著名的“Google Cat”。這個項目的技術已經被應用到了安卓操作系統的語音識別系統上。

是在線教育平台Coursera的聯合創始人，在2008年發起了“Stanford Engineering Everywhere”（SEE）項目，這個項目把斯坦福的許多課程放到網上，供免費學習。NG也教了一些課程，如機器學習課程，包含了他錄製的視頻講座和斯坦福CS299課程的學生材料。

吳恩達加盟百度被認為是中國網際網路公司迄今為止引入的最重要的外援。