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Found 8 papers, 3 papers with code
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Graph-based Visual-Semantic Entanglement Network for Zero-shot Image Recognition
Zero-shot learning uses semantic attributes to connect the search space of unseen objects.
Multiple Attentional Pyramid Networks for Chinese Herbal Recognition
Finally, a new framework for Chinese herbal recognition is proposed as a new application of APN.
Inner-Imaging Networks: Put Lenses into Convolutional Structure
To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement.
Stochastic Region Pooling: Make Attention More Expressive
Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors.
Chinese Herbal Recognition based on Competitive Attentional Fusion of Multi-hierarchies Pyramid Features
According to the characteristics of herbal images, we proposed the competitive attentional fusion pyramid networks to model the features of herbal image, which mdoels the relationship of feature maps from different levels, and re-weights multi-level channels with channel-wise attention mechanism.
Metabolize Neural Network
It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction.
Competitive Inner-Imaging Squeeze and Excitation for Residual Network
In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network.
