Class-wise rationalization: teaching AI to weigh pros and cons
Yang Zhang
Research Staff Member
Who they work with
Yang Zhang is a research scientist at MIT-IBM Watson AI Lab. His research focuses on deep learning for speech, natural language, and other time-series processing. Recently, he has been working on disentanglement techniques for speech and its application to low-resourced languages, as well as improving NLP model interpretability via rationalization. Before joining MIT-IBM Watson AI Lab, Yang is a researcher at IBM Research Yorktown. Yang obtained his Ph.D. degree from University of Illinois at Urbana-Champaign (UIUC). His advisor is Mark Hasegawa-Johnson.
Publications with the MIT-IBM Watson AI Lab
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
Auto-NBA: Efficient and Effective Search Over The Joint Space of Networks, Bitwidths, and Accelerators
Auto-NBA: Efficient and Effective Search Over The Joint Space of Networks, Bitwidths, and Accelerators
Class-wise rationalization: teaching AI to weigh pros and cons
Class-wise rationalization: teaching AI to weigh pros and cons