no code implementations • ACL (dialdoc) 2021 • Boeun Kim, Dohaeng Lee, Sihyung Kim, Yejin Lee, Jin-Xia Huang, Oh-Woog Kwon, Harksoo Kim
In this paper, we propose two models (i. e., a knowledge span prediction model and a response generation model) for the subtask1 and the subtask2.
no code implementations • 12 Mar 2024 • Saurabh Agarwal, Bilge Acun, Basil Homer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
We observe that there is a high amount of redundancy across heads on which tokens they pay attention to.
no code implementations • 22 Dec 2023 • Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency.
1 code implementation • 3 Apr 2023 • Suho Lee, Seungwon Seo, Jihyo Kim, Yejin Lee, Sangheum Hwang
These limitations include a lack of principled ways to determine optimal hyperparameters and performance degradation when the unlabeled target data fail to meet certain requirements such as a closed-set and identical label distribution to the source data.
Source-Free Domain Adaptation Unsupervised Domain Adaptation