Join us for the CSE DSI Machine Learning Seminar with Xinlei Chen, a research scientist at FAIR, Meta AI. Chen will share three pieces of his work centered around the concept of "ablative research". This unique approach involves removing components previously considered essential from the pipeline, rather than adding new techniques, showcasing the potential for innovation in the field.
Research Topics
Chen's presentation will cover a range of topics, including multi-modal image-and-language understanding, self-supervised learning, and transfer learning of pre-trained models. Through his work, he demonstrates the versatility of this meta-level approach across various domains of scientific research.
About Xinlei Chen
Xinlei Chen received his Ph.D. from Carnegie Mellon University in 2018 and his B.S. from Zhejiang University in 2012. His research interests lie in the intersection of computer vision and machine learning. Chen has received prestigious awards for his work, including CVPR best paper honorable mention and ICML outstanding paper honorable mention awards in 2021, as well as two CVPR 2022 best paper finalists, all focusing on self-supervised representation learning.
Don't miss this opportunity to engage with cutting-edge research in machine learning. The seminar will take place at Keller 3-180 or virtually via Zoom.
