Yangzi Guo

Area of research

Artificial Intelligence · Computer Vision and Pattern Recognition

Research interest

Research topics from publications: Network Pruning via Annealing and Direct Sparsity Control; Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks; Generating Compact Tree Ensembles via Annealing; A Study of Local Optima for Learning Feature Interactions using Neural Networks. Representative work: Artificial neural networks (ANNs) especially deep convolutional neural networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vision problems. However, the use of deep neural networks is widely impeded by their intensive computational and memory cost. In this paper, we propose a novel efficient network pruning framework that is suitable for both non-structured and structured channel-level pruning. Our proposed method tightens a sparsity constraint by gradually removing network parameters or filter channels based on a criterion and a schedule. The attractive fact that the network size keeps dropping throughout the iterations makes it s Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irrelevant variables often leads to overfitting. In an attempt to circumvent this issue, a prior knowledge pertaining to what features are relevant and their possible feature interactions can be encoded into these networks. In this work, we use decision trees to capture such relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. This addresses the initialization related concerns of fully connected neural networks. At the same time through feature selection

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