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Faculty Search Candidate: Wei-Lun (Harry) Chao

University of Southern California
Tuesday, February 27, 2018, 4:00 pm

Wei-Lun (Harry) Chao
480 Dreese Labs
2015 Neil Ave, Columbus, Ohio 43210

Transfer learning towards intelligent systems in the wild

Developing intelligent systems for vision and language understanding in the wild has long been a crucial part that people dream about the future. In the past few years, with the accessibility to large-scale data and the advance of machine learning algorithms, vision and language understanding has had significant progress for constrained environments. However, it remains challenging for unconstrained environments in the wild where the intelligent system needs to tackle unseen objects and unfamiliar language usage that it has not been trained on. Transfer learning, which aims to transfer and adapt the learned knowledge from the training environment to a different but related test environment has thus emerged as a promising paradigm to remedy the difficulty.

In this talk, I will present my recent work on transfer learning towards intelligent systems in the wild. I will begin with zero-shot learning, which aims to expand the learned knowledge from seen objects, of which we have training data, to unseen objects, of which we have no training data. I will present an algorithm SynC that can construct classifiers of any object class given its semantic description, even without training data, followed by a comprehensive study on how to apply it to different environments. I will then describe an adaptive visual question answering framework that builds upon the insight of zero-shot learning and can further adapt its knowledge to the new environment given limited information. I will finish my talk with some directions for future research.

Bio: Wei-Lun (Harry) Chao is a Computer Science PhD candidate at University of Southern California, working with Fei Sha. His research interests are in machine learning and its applications to computer vision and artificial intelligence. His recent work has focused on transfer learning towards vision and language understanding in the wild. His earlier research includes work on probabilistic inference, structured prediction for video summarization, and face understanding.

Host: Leon Wang