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NVIDIA GTC San Jose 2017

S7674 - All That Glisters Is Not Convnets: Hybrid Architectures for Faster, Better Solvers

Session Speakers
Session Description

Convolutional neural networks have proven themselves to be very effective parametric learners of complex functions. However, the non-linearities present in conventional networks are not strong; both halves of a (possibly leaky) RELU are linear and the non-linearity is computed independently for each channel. We'll present techniques that create decision tree and RBF units that are designed to respond non-linearly to complex joint distributions across channels. This makes it possible to pack more non-linearity into a small space and this is a particularly valuable replacement for the latter layers of a network - in particular the solver. The result is hybrid networks that outperform conventional pure neural networks that can be trained orders of magnitude more quickly.

Additional Session Information
Intermediate
Talk
Deep Learning and AI, Computer Vision and Machine Vision
Higher Education / Research
50 minutes
Session Schedule