Andrew L. Maas
Andrew L. Maas
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
Rectifier nonlinearities improve neural network acoustic models
AL Maas, AY Hannun, AY Ng
Proc. icml 30 (1), 3, 2013
Learning word vectors for sentiment analysis
A Maas, RE Daly, PT Pham, D Huang, AY Ng, C Potts
Proceedings of the 49th annual meeting of the association for computational …, 2011
Maximum entropy inverse reinforcement learning.
BD Ziebart, AL Maas, JA Bagnell, AK Dey
Aaai 8, 1433-1438, 2008
Recurrent neural networks for noise reduction in robust ASR
A Maas, QV Le, TM O’neil, O Vinyals, P Nguyen, AY Ng
Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior
BD Ziebart, AL Maas, AK Dey, JA Bagnell
Proceedings of the 10th international conference on Ubiquitous computing …, 2008
Lexicon-Free Conversational Speech Recognition with Neural Networks
AL Maas, Z Xie, D Jurafsky, AY Ng
North American Chapter of the Association for Computational Linguistics (NAACL), 2015
First-pass large vocabulary continuous speech recognition using bi-directional recurrent DNNs
AY Hannun, AL Maas, D Jurafsky, AY Ng
arXiv preprint arXiv:1408.2873, 2014
Building DNN acoustic models for large vocabulary speech recognition
AL Maas, P Qi, Z Xie, AY Hannun, CT Lengerich, D Jurafsky, AY Ng
Computer Speech & Language 41, 195-213, 2017
Human Behavior Modeling with Maximum Entropy Inverse Optimal Control.
BD Ziebart, AL Maas, JA Bagnell, AK Dey
AAAI spring symposium: human behavior modeling 92, 2009
A probabilistic model for semantic word vectors
AL Maas, AY Ng
NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 1-8, 2010
Spectral chinese restaurant processes: Nonparametric clustering based on similarities
R Socher, A Maas, C Manning
Proceedings of the Fourteenth International Conference on Artificial …, 2011
Offering verified credentials in massive open online courses: MOOCs and technology to advance learning and learning research (Ubiquity symposium)
A Maas, C Heather, C Do, R Brandman, D Koller, A Ng
Ubiquity 2014 (May), 1-11, 2014
Sentiment expression conditioned by affective transitions and social forces
M Sudhof, A Goméz Emilsson, AL Maas, C Potts
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
Retrofitting distributional embeddings to knowledge graphs with functional relations
BJ Lengerich, AL Maas, C Potts
arXiv preprint arXiv:1708.00112, 2017
Unsupervised feature learning and deep learning
A Ng, J Ngiam, CY Foo, Y Mai, C Suen, A Coates, A Maas, A Hannun, ...
Technical report, Stanford University, 2013
Word-level acoustic modeling with convolutional vector regression
AL Maas, SD Miller, TM O’neil, AY Ng, P Nguyen
Proc. ICML Workshop Representation Learn, 2012
Recurrent neural network feature enhancement: The 2nd CHiME challenge
AL Maas, TM O’Neil, AY Hannun, AY Ng
Proceedings The 2nd CHiME Workshop on Machine Listening in Multisource …, 2013
One-shot learning with bayesian networks
A Maas, C Kemp
Carnegie Mellon University, 2009
Sequence to sequence transformations for speech synthesis via recurrent neural networks
DLW Hall, D Klein, D Roth, L Gillick, A Maas, S Wegmann
US Patent App. 15/792,236, 2018
Multi-dimensional sentiment analysis with learned representations
AL Maas, AY Ng, C Potts
Stanford University. Zugriff am 9, 2014, 2011
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Artikkelit 1–20