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NIPS_paper_2016.bbl
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\begin{thebibliography}{21}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
\providecommand{\doi}[1]{doi: #1}\else
\providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
\bibitem[Wu et~al.()Wu, Khosla, Yu, Zhang, Tang, and Xiao]{3DShapeNets}
Z.~Wu, A.~Khosla, F.~Yu, L.~Zhang, X.~Tang, and J.~Xiao.
\newblock 3d shapenets: A deep representation for volumetric shapes.
\newblock In \emph{CVPR 2015}.
\bibitem[Su et~al.()Su, Maji, Kalogerakis, and E.Learned-Miller]{MVCNN}
H.~Su, S.~Maji, E.~Kalogerakis, and E.Learned-Miller.
\newblock Multi-view convolutional neural networks for 3d shape recognition.
\newblock In \emph{ICCV 2015}.
\bibitem[Johns et~al.()Johns, Leutenegger, and Davison]{Pairwise}
E.~Johns, S.~Leutenegger, and A.~J. Davison.
\newblock Pairwise decomposition of image sequences for active multi-view
recognition.
\newblock In \emph{CVPR 2016}.
\bibitem[Hegde and Zadeh(2016)]{FusionNets}
V.~Hegde and R.~Zadeh.
\newblock Fusionnet: 3d object classification using multiple data
representations.
\newblock arXiv Preprint arXiv: 1607.05695, 2016.
\bibitem[Maturana and Scherer()]{VoxNet}
D.~Maturana and S.~Scherer.
\newblock Voxnet: A 3d convolutional neural network for real-time object
recognition.
\newblock In \emph{IROS 2015}.
\bibitem[Sedaghat et~al.(2016)Sedaghat, Zolfaghari, and Brox]{ORION}
N.~Sedaghat, M.~Zolfaghari, and T.~Brox.
\newblock Orientation-boosted voxel nets for 3d object recognition.
\newblock arXiv Preprint arXiv: 1604.03351, 2016.
\bibitem[Kingma and Welling()]{VAE}
D.P. Kingma and M.~Welling.
\newblock Auto-encoding variational bayes.
\newblock In \emph{ICLR 2014}.
\bibitem[Team(2016)]{Theano}
The Theano~Development Team.
\newblock Theano: A python framework for fast computation of mathematical
expressions.
\newblock arXiv Preprint arXiv: 1605.02688, 2016.
\bibitem[Clevert et~al.()Clevert, Unterthiner, and Hochreiter]{ELU}
D-A. Clevert, T.~Unterthiner, and S.~Hochreiter.
\newblock Fast and accurate deep network learning by exponential linear units
(elus).
\newblock In \emph{ICLR 2016}.
\bibitem[Dumoulin and Visin(2016)]{arithmetic}
V.~Dumoulin and F.~Visin.
\newblock A guide to convolution arithmetic for deep learning.
\newblock arXiv Preprint arXiv: 1603.07285, 2016.
\bibitem[Glorot and Bengio()]{Glorot}
X.~Glorot and Y.~Bengio.
\newblock Understanding the difficulty of training deep feedforward neural
networks.
\newblock In \emph{AISTATS 2010}.
\bibitem[Ioffe and Szegedy()]{Bnorm}
S.~Ioffe and C.~Szegedy.
\newblock Batch normalization: Accelerating deep network training by reducing
internal covariate shift.
\newblock In \emph{ICML 2015}.
\bibitem[Sutskever et~al.()Sutskever, Martens, Dahl, and Hinton]{Nesterov}
I.~Sutskever, J.~Martens, G.~Dahl, and G.~Hinton.
\newblock On the importance of initialization and momentum in deep learning.
\newblock In \emph{ICML 2013}.
\bibitem[Yumer et~al.()Yumer, Asente, Mech, and Kara]{GUI_REF}
M.~Yumer, P.~Asente, R.~Mech, and L.~Kara.
\newblock Procedural modeling using autoencoder networks.
\newblock In \emph{UIST 2015}.
\bibitem[Schroeder et~al.(2006)Schroeder, Martin, and Lorenson.]{VTK}
W.~Schroeder, K.~Martin, and B.~Lorenson.
\newblock \emph{The Visualization Toolkit,}.
\newblock Kitware, 4 edition, 2006.
\bibitem[Szegedy et~al.(2016)Szegedy, Ioffe, and Vanhoucke]{Inception}
C.~Szegedy, S.~Ioffe, and V.~Vanhoucke.
\newblock Inception-v4, inception-resnet and the impact of residual connections
on learning.
\newblock arXiv Preprint arXiv: 1602.07261, 2016.
\bibitem[He et~al.()He, Zhang, Ren, and Sun]{ResNet}
K.~He, X.~Zhang, S.~Ren, and J.~Sun.
\newblock Deep residual learning for image recognition.
\newblock In \emph{CVPR 2016}.
\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{PreActivation}
K.~He, X.~Zhang, S.~Ren, and J.~Sun.
\newblock Identity mappings in deep residual networks.
\newblock arXiv Preprint arXiv: 1603.05027, 2016.
\bibitem[Huang et~al.(2016)Huang, Sun, Liu, Sedra, and
Weinberger]{StochasticDepth}
G.~Huang, Y.~Sun, Z.~Liu, D.~Sedra, and K.~Q. Weinberger.
\newblock Deep networks with stochastic depth.
\newblock arXiv Preprint arXiv: 1603.09382, 2016.
\bibitem[Saxe et~al.()Saxe, McClelland, and Ganguli]{orthog}
A.M. Saxe, J.~L. McClelland, and S.~Ganguli.
\newblock Exact solutions to the nonlinear dynamics of learning in deep linear
neural networks.
\newblock In \emph{ICLR 2014}.
\bibitem[Kingma and Ba(2014)]{Adam}
D.P. Kingma and J.~Ba.
\newblock Adam: A method for stochastic optimization.
\newblock arXiv Preprint arXiv: 1412.6980, 2014.
\end{thebibliography}