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ORCID1. FE@NIPS 2015: Montreal, Canada
- Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, FE 2015, co-located with the 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), Montreal, Canada, December 11-12, 2015. JMLR Workshop and Conference Proceedings 44, JMLR.org 2015
Accepted Papers
- Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
A Survey of Modern Questions and Challenges in Feature Extraction. 1-18 - Hassan Ashtiani, Ali Ghodsi:
A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis. 19-29 - Yuval Atzmon, Uri Shalit, Gal Chechik:
Learning Sparse Metrics, One Feature at a Time. 30-48 - Elnaz Barshan, Paul W. Fieguth:
Stage-wise Training: An Improved Feature Learning Strategy for Deep Models. 49-59 - Xue-wen Chen, Melih S. Aslan, Kunlei Zhang, Thomas S. Huang:
Learning Multi-channel Deep Feature Representations for Face Recognition. 60-71 - Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri:
Kernel Extraction via Voted Risk Minimization. 72-89 - Xia Cui, Ying Lu, Heng Peng:
A Computationally Efficient Method for Estimating Semi Parametric Regression Functions. 90-102 - Dimitrios Giannakis, Joanna Slawinska
, Zhizhen Zhao:
Spatiotemporal Feature Extraction with Data-Driven Koopman Operators. 103-115 - Furong Huang, Animashree Anandkumar:
Convolutional Dictionary Learning through Tensor Factorization. 116-129 - Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar:
FEAST at Play: Feature ExtrAction using Score function Tensors. 130-144 - Melih Kandemir, Fred A. Hamprecht:
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. 145-159 - Minyoung Kim, Luca Rigazio:
Deep Clustered Convolutional Kernels. 160-172 - Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft:
Theory and Algorithms for the Localized Setting of Learning Kernels. 173-195 - Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? 196-212 - Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar:
Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns. 213-225 - Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus:
Generalization Bounds for Supervised Dimensionality Reduction. 226-241 - Henry W. J. Reeve, Gavin Brown:
Modular Autoencoders for Ensemble Feature Extraction. 242-259 - Gil I. Shamir:
Minimum description length (MDL) regularization for online learning. 260-276 - Jonathan P. Williams, Ying Lu:
Covariance Selection in the Linear Mixed Effect Mode. 277-291
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