Accepted Papers

ACCEPTED PAPERS


Submitted talk 1, 10:00-10:20


Discovering Salient Features via Adaptively Chosen Comparisons

James Y. Zou, Kamalika Chaudhuri, Adam T. Kalai


Submitted talk 2 12:00-12:20


Convergent Learning: Do different neural networks learn similar features?

Yixuan Li, Jason Yosinski, Jeff Clune, John Hopcroft, Hod Lipson


Poster Session 1, 12:20-14:50


On Visual Feature Representations for Transition State Learning in Robotic Task Demonstrations

Animesh Garg, Sanjay Krishnan, Adithyavairavan Murali,  Florian T. Pokorny, Pieter Abbeel, Trevor Darrell, Ken Goldberg


FEAST at Play: Feature ExtrAction using Score function Tensors

Majid Janzamin, Hanie Sedghi, U.N. Niranjan, Animashree Anandkumar


Minimum Description Length (MDL) Regularization for Online Learning

Gil I. Shamir


Convergent Learning: Do different neural networks learn similar features?

Yixuan Li, Jason Yosinski, Jeff Clune, John Hopcroft, Hod Lipson


A Computationally Efficient Method for Estimating Semi-parametric Regression Functions

Xia Cui, Ying Lu, Heng Peng


Learning Sparse Metrics, One Feature at a Time

Yuval Atzmon, Uri Shalit, Gal Chechik


Covariance Selection in the Linear Mixed Effect Model

Jonathan P Williams,Ying Lu


A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis

Hassan Ashtiani, Ali Ghodsi


Compact Models for Large-scale Non-linear Learning via Random Feature Selection

Avner May, Michael Collins, Daniel Hsu, Brian Kingsbury


Deep Clustered Convolutional Kernels

Minyoung Kim, Luca Rigazio


Theory and Algorithms for the Localized Setting of Learning Kernels

Yunwen Lei, Alexander Binder, Ürün Dogan  Marius Kloft


Kernel Extraction via Voted Risk Minimization

Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri


Poster Session 2, 17:10-18:30


Discovering Salient Features via Adaptively Chosen Comparisons

James Y. Zou, Kamalika Chaudhuri, Adam T. Kalai


Robust Discriminative Clustering with Sparse Regularizers

Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach


Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns

Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar


Generalization Bounds for Supervised Dimensionality Reduction

Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus


Spatiotemporal Feature Extraction with Data-Driven Koopman Operators

Dimitrios Giannakis, Joanna Slawinska, Zhizhen Zhao


Convolutional Dictionary Learning through Tensor Factorization

Furong Huang, Animashree Anandkumar


The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors

Melih Kandemir, Fred A. Hamprecht


Learning Multi-channel Deep Feature Representations for Face Recognition

Xue-wen Chen, Melih S. Aslan, Kunlei Zhang,Thomas S. Huang


Modular Autoencoders for Ensemble Feature Extraction

Henry W J Reeve, Gavin Brown


Linear-time Learning on Distributions with Approximate Kernel Embeddings

Dougal J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider


Stage-wise Training: An Improved Feature Learning Strategy for Deep Models

Elnaz Barshan, Paul Fieguth


POSTER INSTRUCTIONS


Authors are required to attach their posters to the wall with scotch tape before their poster session, the tape will be provided. We recommend using standard NIPS poster format 8 ft. wide by 4ft. tall (~2.4m x 1.2m), this is "landscape" style. However, since the posters will be attached to the wall, the authors may choose virtually any poster size. It is recommended to print your posters in advance before NIPS using professional printing service (e.g. Fedex, Staples). There are also options for printing posters in Montreal during NIPS:

  • The nearest Staples Business Depot to the Montreal Convention Center is probably the best bet for printing on site.  Their phone number is +1 514-879-1515.  They need 24 hour notice, call to confirm they can process your online order.

  • GES also provides printing services: 514-861-9694 x 19, is near the Convention Center:

  • MP Photo services is located at 1030 Rue Chenneville, Montréal.   Call 514 861-8541 with questions and orders.

SUBMITTED TALK INSTRUCTIONS


Please email the pdf of your presentation by Dec 7 to featureextraction2015submit@gmail.com as well as bring a backup copy on a flash drive to the workshop.


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