MCL received a grant from the US Army Research Laboratory (ARL) recently for joint research on theory and applications of artificial intelligence (AI) and machine learning (ML) technologies. Through this support, MCL researchers will collaborate with ARL researchers to conduct fundamental and applied research in the next 4 years.
The fundamental research targets at explainable neural networks. Cybenko and Hornik et al. proved that the multi-layer perceptron (MLP) is a universal approximator in late 80s, which appears to be the most important theoretic result even up to now. However, for a given dataset, they do not provide a constructive procedure to build the MLP. We will investigate a systematic way to specify an MLP architecture and determine its model parameters. Resource-rich and resource-scarce networks refer to those have abundant and fewer model parameters, respectively. The objective of the stress test study is to understand the behavior of the transition of a network from a resource-rich one to a resource-scarce one. We would like to understand the behavior transition in several areas, including model sensitivity to different weight initializations, classification accuracy, overfitting, etc.
The applied research targets at spatial-temporal attention and semantic scene understanding via successive subspace learning (SSL). To extract key spatial-temporal information from visual data, facilitates video processing and understanding down-stream tasks. For example, an image contains one or several objects. Object detection and recognition is critical to image understanding. Also, motion provides cues for object tracking in video understanding. The sponsored research will also focus on two issues on multi-modality data, say, those obtained by RGB, depth and infrared sensors: 1) representation of multi-domain data and 2) understanding of multi-domain data.
—- by Dr. C.-C. Jay Kuo
