I am a Nanyang Associate Professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. My research interests lie mainly in Computational Narrative Intelligence, Multi-modal Learning, and Machine Learning.
Story is a powerful tool for communication, an exhibit of creativity, and a timeless form of entertainment. Computational Narrative Intelligence (CNI) aims to create intelligent machines that can understand and create stories, manage interactive narratives, and respond appropriately to stories told to them. I have made contributions to all major areas of CNI, ranging from story generation and interactive narratives to human cognition, from learning story knowledge to story understanding.
I believe that in order to simulate human intelligence, artificial intelligence must first acquire human-level knowledge from an approximation of the human experience, which is inherently multimodal. Further, inspired by the observation that human intelligence emerges from the interaction of multiple cognitive processes, I am interested in understanding the interactions between different neural network components and developing techniques that coordinate the learning of different neural network components beyond simplistic stochastic gradient descent.
Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, and Chunyan Miao. Noise-resistant Deep Metric Learning with Ranking-based Instance Selection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
TL;DR: We introduce a simple, efficient, and (we believe) the first technique for deep metric learning under noisy training data; the method outperforms 12 baseline methods under both synthetic and natural noise.Paper Supplemental Code bibtex
Yuanyuan Chen, Boyang Li, Han Yu, Pengcheng Wu, and Chunyan Miao. HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks. The AAAI Conference on Artificial Intelligence (AAAI). 2021.
TL;DR: We provide an approximate hypergradient method for estimating how training data contribute to individual network predictions and a theoretical bound on the approximation error.Paper Supplemental Code bibtex
Jianan Wang, Boyang Li, Xiangyu Fan, Jing Lin, and Yanwei Fu. Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions. The IEEE Winter Conference on Applications of Computer Vision (WACV). 2021.
TL;DR: We present several tricks that improve data efficiency of the NeuMATCH network, which aligns video and textual sequences.Paper Supplemental Video Code & Data bibtex
Adam Noack, Isaac Ahern, Dejing Dou, and Boyang Li. An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness Springer Nature Computer Science. 2020.
TL;DR: Does the interpretability of neural networks imply robustness against adversarial attack? We provide some positive empirical evidence.Paper Code bibtex
Hannah Kim, Denys Katerenchuk, Daniel Billet, Jun Huan, Haesun Park, and Boyang Li. Understanding Actors and Evaluating Personae with Gaussian Embeddings. The AAAI Conference on Artificial Intelligence (AAAI). 2019.
TL;DR: We computationally model movie casting decisions and actors' versatility.Paper Code & Data bibtex
Pelin Dogan, Boyang Li, Leonid Sigal, Markus Gross. A Neural Multi-sequence Alignment TeCHnique (NeuMATCH). The Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
TL;DR: We propose the first end-to-end optimizable network for aligning video and text sequences.Paper Data bibtex
Ng Annalyn, Maarten Bos, Leonid Sigal, Boyang Li. Predicting Personality from Book Preferences with User-Generated Content Labels. IEEE Transaction on Affective Computing. 2018.
TL;DR: We can infer your personality from the books you read.Paper bibtex
I have multiple open positions for Ph.D. students, postdocs, and research engineers. If you are interested, please send me your CV.
- Mar 2020: One long paper accepted at NAACL 2021.
- Mar 2020: Received the NRF Fellowship funding of 3M SGD.
- Feb 2020: One paper accepted at CVPR 2021. (acceptance rate = 23.7%)
- Dec 2020: Served as a reviewer for CVPR 2021.
- Dec 2020: Hypergradient Data Relevance Analysis (HyDRA) accepted at AAAI 2021 (acceptance rate = 21%).
- Nov 2020: An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness accepted by Springer Nature Computer Science.
- Nov 2020: Paper on improving data efficiency of multimodal sequence alignment accepted at WACV 2021.