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Yoram Singer | Nevena Lazic | Amit Daniely | Kunal Talwar | Y. Singer | Kunal Talwar | Amit Daniely | Nevena Lazic | N. Lazic
[1] Bernard Chazelle,et al. The Fast Johnson--Lindenstrauss Transform and Approximate Nearest Neighbors , 2009, SIAM J. Comput..
[2] Mark Dredze,et al. Small Statistical Models by Random Feature Mixing , 2008, ACL 2008.
[3] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[4] W. B. Johnson,et al. Extensions of Lipschitz mappings into Hilbert space , 1984 .
[5] Nevena Lazic,et al. Context-Dependent Fine-Grained Entity Type Tagging , 2014, ArXiv.
[6] Daniel M. Kane,et al. Sparser Johnson-Lindenstrauss Transforms , 2010, JACM.
[7] Yixin Chen,et al. Compressing Convolutional Neural Networks , 2015, ArXiv.
[8] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[9] Shih-Fu Chang,et al. An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[10] David P. Woodruff,et al. Low rank approximation and regression in input sparsity time , 2013, STOC '13.
[11] Kilian Q. Weinberger,et al. Feature hashing for large scale multitask learning , 2009, ICML '09.
[12] Richard G. Baraniuk,et al. A deep learning approach to structured signal recovery , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[13] Moses Charikar,et al. Finding frequent items in data streams , 2004, Theor. Comput. Sci..
[14] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[15] Partha Pratim Talukdar,et al. Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch , 2013, AISTATS.
[16] Santosh S. Vempala,et al. An algorithmic theory of learning: Robust concepts and random projection , 1999, Machine Learning.
[17] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[18] Andrew R. Barron,et al. Approximation and estimation bounds for artificial neural networks , 2004, Machine Learning.
[19] Rafail Ostrovsky,et al. Rademacher Chaos, Random Eulerian Graphs and The Sparse Johnson-Lindenstrauss Transform , 2010, ArXiv.
[20] Noga Alon,et al. The Space Complexity of Approximating the Frequency Moments , 1999 .
[21] Harald Niederreiter,et al. Probability and computing: randomized algorithms and probabilistic analysis , 2006, Math. Comput..
[22] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[24] Rich Caruana,et al. Do Deep Nets Really Need to be Deep? , 2013, NIPS.
[25] Dimitris Achlioptas,et al. Database-friendly random projections: Johnson-Lindenstrauss with binary coins , 2003, J. Comput. Syst. Sci..
[26] Anirban Dasgupta,et al. A sparse Johnson: Lindenstrauss transform , 2010, STOC '10.
[27] Aryeh Kontorovich. A Universal Kernel for Learning Regular Languages , 2007, MLG.
[28] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[29] Misha Denil,et al. Predicting Parameters in Deep Learning , 2014 .
[30] Graham Cormode,et al. An improved data stream summary: the count-min sketch and its applications , 2004, J. Algorithms.
[31] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[32] David P. Woodruff,et al. Lower bounds for sparse recovery , 2010, SODA '10.
[33] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[34] Rasmus Pagh,et al. Fast and scalable polynomial kernels via explicit feature maps , 2013, KDD.
[35] Gregory J. Wolff,et al. Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.
[36] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[37] Graham Cormode,et al. Summarizing and Mining Skewed Data Streams , 2005, SDM.
[38] Jirí Matousek,et al. On variants of the Johnson–Lindenstrauss lemma , 2008, Random Struct. Algorithms.
[39] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[40] Pushmeet Kohli,et al. Memory Bounded Deep Convolutional Networks , 2014, ArXiv.
[41] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.