"Efficient memory-usage techniques in deep neural networks via a graph-based approach"

Qualcomm Conference Room (EBU-1)

Salimeh Yasaei Sekeh - University of Maine 

Seminar Abstract

Dr. Salimeh Yasaei Sekeh is the Assistant Professor of Computer Science in the School of Computing and Information Sciences at the University of Maine. Her research focuses on designing and analyzing machine learning algorithms, deep learning techniques, applications of machine learning approaches in real-time problems, data mining, pattern recognition, and network structure learning with applications in biology. This talk introduces two new and efficient deep memory usage techniques based on the geometric dependency criterion. This first technique is called Online Streaming Deep Feature Selection. This technique is based on a novel supervised streaming setting and it measures deep feature relevance while maintaining a minimal deep feature subset with relatively high classification performance and less memory requirement. The second technique is called Geometric Dependency-based Neuron Trimming. This technique is a data-driven pruning method that evaluates the relationship between nodes in consecutive layers. In this approach, a new dependency-based pruning score removes neurons with least importance, and then the network is fine-tuned to retain its predictive power. Both methods are evaluated on several data sets with multiple CNN models and demonstrated to achieve significant memory compression compared to the baselines.