2 citations · 5 across the 4 of their papers we have counts for
4 papers
SA-GD: Improved Gradient Descent Learning Strategy with Simulated Annealing
Zhicheng Cai
Gradient descent algorithm is the most utilized method when optimizing machine learning issues. However, there exists many local minimums and saddle points in the loss function, es…
Interflow: Aggregating Multi-layer Feature Mappings with Attention Mechanism
Zhicheng Cai
Traditionally, CNN models possess hierarchical structures and utilize the feature mapping of the last layer to obtain the prediction output. However, it can be difficulty to settle…
Jitter: Random Jittering Loss Function
Zhicheng Cai, Chenglei Peng, Sidan Du
Regularization plays a vital role in machine learning optimization. One novel regularization method called flooding makes the training loss fluctuate around the flooding level. It…
Reborn Mechanism: Rethinking the Negative Phase Information Flow in Convolutional Neural Network
Zhicheng Cai, Kaizhu Huang, Chenglei Peng
This paper proposes a novel nonlinear activation mechanism typically for convolutional neural network (CNN), named as reborn mechanism. In sharp contrast to ReLU which cuts off the…