If it still exists, it will have an incredible impact.The computational cost for long sequences is often very high. When long sequences become longer, they will be forgotten. The training cleverly uses neural networks to solve the shortcomings. The author introduces that the author's contribution to this study is listed at the end of the paper. The core authors are, and K. is a postdoctoral fellow in computer science at Stanford University. His supervisors are, and Kj. He previously completed a doctorate in electrical engineering science at the University of California, Berkeley.
His supervisors are and. He also received ahe introduced lithuania phone numbers that his research focus is an algorithm framework called test time training (-). The core idea is that each test instance defines its own learning problem and has its own generalization goal. This is usually achieved using self-supervised learning to train a different model for each instance on the fly. In the latest research, he co-started this project with K. He has been in charge of the project full-time since January. He proposed the conceptual framework of the project and designed - and the dual form ().
He is a second-year graduate student supervisor is Professor. His own research interests are mainly deep learning and computer vision. He worked as a visiting student with PhD and other mentor friends in the team of Professor Stanford University. Prior to this, he received a bachelor's degree from the University of Electronic Science and Technology of China. Before January, he was a major contributor to the early code bases that shaped the latest project. K K is an undergraduate student in the Department of Electronic Engineering Science of K. He joined the project full-time in January and co-led the development of the current code base.