dense layer
Pelee: A Real-Time Object Detection System on Mobile Devices
Jun Wang, Tanner Bohn, Charles Ling
Neural Information Processing Systems http://nips.cc/
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- North America > Canada > Ontario > Middlesex County > London (0.04)
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
Oisín Moran, Piergiorgio Caramazza, Daniele Faccio, Roderick Murray-Smith
The experimental data is used to train complex-weighted models with a range of regularisation approaches.
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- North America > Canada > Quebec > Montreal (0.04)
CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations
Human communication has a multi-modal and multi-affect nature.
- Asia > China > Hong Kong (0.05)
- Asia > Myanmar > Tanintharyi Region > Dawei (0.04)
- Asia > China > Tianjin Province > Tianjin (0.04)
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A Experiment Details and Complete Results
A.2 Model Architectures In this section we describe in detail each of the model architectures we use in our experiments. Our small ConvNet consists of the following layers: A convolutional layer with 32 kernels of size 3 3 and ReLU activation. A max pooling layer with pool size 2 2. A flatten layer. For inputs of shape 32 32 3, this model has 21,697 parameters. Our large ConvNet model consists of the following layers: A convolutional layer with 32 kernels of size 3 3, padding, and ReLU activation.
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- Asia > China > Shaanxi Province > Xi'an (0.04)
- South America > Chile > Santiago Metropolitan Region > Santiago Province > Santiago (0.04)
- Europe > Poland > Masovia Province > Warsaw (0.04)
