BibSLEIGH — Andrew_Y

Travelled to:

1 × China
1 × Germany
1 × Japan
1 × Slovenia
1 × United Kingdom
12 × USA
2 × Finland
3 × Canada

Collaborated with:

A.Coates P.Abbeel J.Z.Kolter ∅ R.Raina D.Koller M.I.Jordan S.J.Russell H.Lee J.Ngiam C.D.Manning T.Wang D.J.Wu Z.Chen P.W.Koh C.Foo C.B.Do A.Madhavan M.Quigley E.Minkov W.W.Cohen J.Michels A.Saxena S.Shalev-Shwartz Y.Singer K.Toutanova A.X.Zheng D.Harada Q.V.Le B.Suresh R.Socher C.C.Lin R.B.Grosse R.Ranganath A.McCallum R.Rosenfeld T.M.Mitchell Y.Gu C.DuHadway A.Battle B.Packer T.Kremenek P.Twohey G.Back D.R.Engler S.T.Dumais M.Banko E.Brill J.J.Lin B.Huval B.C.Catanzaro A.Lahiri B.Prochnow A.Khosla M.Kim J.Nam A.M.Saxe M.Bhand M.Brzozowski K.Carattini S.R.Klemmer P.Mihelich J.Hu M.Ranzato R.Monga M.Devin G.Corrado K.Chen J.Dean B.Carpenter C.Case S.Satheesh

Talks about:

learn (26) featur (7) deep (6) use (6) unsupervis (5) reinforc (5) algorithm (3) network (3) select (3) model (3)

Person: Andrew Y. Ng

DBLP: Ng:Andrew_Y=

Contributed to:

2013

2012

2011

2009

2008

2007

2006

2005

2004

2002

2001

2000

1999

1998

1997

Wrote 37 papers:

ICML-c3-2013-CoatesHWWCN #learning #off the shelf: Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.
KDD-2013-NgK #education #online: The online revolution: education for everyone (AYN, DK), p. 2.
ICML-2012-LeRMDCCDN #learning #scalability #using: Building high-level features using large scale unsupervised learning (QVL, MR, RM, MD, GC, KC, JD, AYN), p. 69.
ICPR-2012-WangWCN #network #recognition: End-to-end text recognition with convolutional neural networks (TW, DJW, AC, AYN), pp. 3304–3308.
ICDAR-2011-CoatesCCSSWWN #detection #image #learning #recognition: Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning (AC, BC, CC, SS, BS, TW, DJW, AYN), pp. 440–445.
ICML-2011-CoatesN #encoding: The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (AC, AYN), pp. 921–928.
ICML-2011-LeNCLPN #learning #on the #optimisation: On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICML-2011-NgiamCKN #energy #learning #modelling: Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICML-2011-NgiamKKNLN #learning #multimodal: Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICML-2011-SaxeKCBSN #learning #on the #random: On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICML-2011-SocherLNM #natural language #network #parsing #recursion: Parsing Natural Scenes and Natural Language with Recursive Neural Networks (RS, CCYL, AYN, CDM), pp. 129–136.
ICML-2009-FooDN #algorithm #learning #multi: A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.
ICML-2009-KolterN #polynomial: Near-Bayesian exploration in polynomial time (JZK, AYN), pp. 513–520.
ICML-2009-KolterN09a #difference #feature model #learning: Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.
ICML-2009-LeeGRN #learning #network #scalability: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.
ICML-2009-RainaMN #learning #scalability #using: Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.
ICML-2008-CoatesAN #learning #multi: Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICML-2008-KolterCNGD #learning #programming: Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICML-2007-RainaBLPN #learning #self: Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
CHI-2006-BrzozowskiCKMHN #named #scheduling: groupTime: preference based group scheduling (MB, KC, SRK, PM, JH, AYN), pp. 1047–1056.
ICML-2006-AbbeelQN #learning #modelling #using: Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
ICML-2006-RainaNK #learning #using: Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
OSDI-2006-KremenekTBNE #nondeterminism #specification: From Uncertainty to Belief: Inferring the Specification Within (TK, PT, GB, AYN, DRE), pp. 161–176.
SIGIR-2006-MinkovCN #ambiguity #email #graph #using: Contextual search and name disambiguation in email using graphs (EM, WWC, AYN), pp. 27–34.
ICML-2005-AbbeelN #learning: Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
ICML-2005-MichelsSN #learning #using: High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
ICML-2004-PieterN #learning: Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICML-2004-Shalev-ShwartzSN #learning #online #pseudo: Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICML-2004-ToutanovaMN #dependence #learning #modelling #random #word: Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
SIGIR-2002-DumaisBBLN #question #web: Web question answering: is more always better? (STD, MB, EB, JJL, AYN), pp. 291–298.
ICML-2001-NgJ #classification #convergence #feature model: Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection (AYN, MIJ), pp. 377–384.
SIGIR-2001-ZhengNJ #algorithm #analysis: Stable Algorithms for Link Analysis (AXZ, AYN, MIJ), pp. 258–266.
ICML-2000-NgR #algorithm #learning: Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
ICML-1999-NgHR #policy #theory and practice: Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping (AYN, DH, SJR), pp. 278–287.
ICML-1998-McCallumRMN #classification: Improving Text Classification by Shrinkage in a Hierarchy of Classes (AM, RR, TMM, AYN), pp. 359–367.
ICML-1998-Ng #feature model #learning #on the: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.
ICML-1997-Ng: Preventing “Overfitting” of Cross-Validation Data (AYN), pp. 245–253.