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Found 6 papers, 0 papers with code
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement
In addition to conventional compression techniques, e. g., weight pruning and quantization, removing unimportant activations can reduce the amount of data communication and the computation cost.
Feedback Learning for Improving the Robustness of Neural Networks
Recent research studies revealed that neural networks are vulnerable to adversarial attacks.
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning
With the rapid scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency.
Integral Pruning on Activations and Weights for Efficient Neural Networks
With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment.
Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes.
Learning with Target Prior
In this work, we describe a new learning scheme for parametric learning, in which the target variables $\y$ can be modeled with a prior model $p(\y)$ and the relations between data and target variables are estimated through $p(\y)$ and a set of uncorresponded data $\x$ in training.
