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We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape.
An efficient method for finding the minimum of a function of several variables without calculating derivatives
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The representation and matching of pictorial structures
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Constructive solid geometry for polyhedral objects
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Recognition-by-Components: A Theory of Human Image Understanding
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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
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Policy Gradient Methods for Reinforcement Learning with Function Approximation
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Introduction to Automata Theory, Languages, and Computation
J. E. Hopcroft, R. Motwani, and U. J. D · 2001
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Pictorial structures for object recognition
P. F. Felzenszwalb and D. P. Huttenlocher · 2005
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Vision as Bayesian inference: analysis by synthesis?
A. Yuille and D. Kersten · 2006
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A Connection Between Partial Symmetry and Inverse Procedural Modeling
M. Bokeloh, M. Wand, and H.-P. Seidel · 2010
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Detecting people using mutually consistent poselet activations
L. Bourdev, S. Maji, T. Brox, and J. Malik · 2010
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Shape Grammar Parsing via Reinforcement Learning
O. Teboul, I. Kokkinos, L. Simon, P. Koutsourakis, and N. Paragios · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
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Learning Design Patterns with Bayesian Grammar Induction
J. Talton, L. Yang, R. Kumar, M. Lim, N. Goodman, and R. Měch · 2012
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Bayesian Grammar Learning for Inverse Procedural Modeling
A. Martinovic and L. Van Gool · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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W. Zaremba and I. Sutskever · 2014
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Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
A. Joulin and T. Mikolov · 2015
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Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
D. Ritchie, A. Thomas, P. Hanrahan, and N. D. Goodman · 2016
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Learning Simple Algorithms from Examples
W. Zaremba, T. Mikolov, A. Joulin, and R. Fergus · 2016
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DeepCoder: Learning to Write Programs
M. Balog, A. L. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow · 2017
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M. Denil, S. Gómez Colmenarejo, S. Cabi, D. Saxton, and N. De Freitas · 2017
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Learning to generate chairs, tables and cars with convolutional networks
A. Dosovitskiy, J. T. Springenberg, M. Tatarchenko, and T. Brox · 2017
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Picture: A probabilistic programming language for scene perception
T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. Whitney, P. Kohli, and J. B. Tenenbaum · 2015
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Controlling Procedural Modeling Programs with Stochastically-ordered Sequential Monte Carlo
D. Ritchie, B. Mildenhall, N. D. Goodman, and P. Hanrahan · 2015
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Neural Module Networks
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, K. Kavukcuoglu, and G. Hinton · 2016
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K. Ellis, D. Ritchie, A. Solar-Lezama, and J. B. Tenenbaum · 2017
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A Point Set Generation Network for 3D Object Reconstruction from a Single Image
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Learning to reason: End-to-end module networks for visual question answering
R. Hu, J. Andreas, M. Rohrbach, T. Darrell, and K. Saenko · 2017
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Shape Synthesis from Sketches via Procedural Models and Convolutional Networks
H. Huang, E. Kalogerakis, E. Yumer, and R. Mech · 2017
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Inferring and Executing Programs for Visual Reasoning
J. Johnson, B. Hariharan, L. Van Der Maaten, J. Hoffman, L. Fei-Fei, C. L. Zitnick, and R. Girshick · 2017
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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
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Learning Shape Abstractions by Assembling Volumetric Primitives
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Neural Scene De-rendering
J. Wu and J. B. Tenenbaum · 2017
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3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
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