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Memory Bounded Deep Convolutional Networks
Maxwell D. Collins, Pushmeet Kohli
In this work, we investigate the use of sparsity-inducing regularizers during training of Convolution Neural Networks (CNNs). These regularizers encourage that fewer connections in…
Consensus Message Passing for Layered Graphical Models
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow +2
Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is…
Inverse Graphics with Probabilistic CAD Models
Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli +1
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perce…
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Roman Shapovalov, Dmitry Vetrov, Anton Osokin +1
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper w…