1 code implementation • 3 Apr 2024 • Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang, Baohua Dong, Ran Lin, Ruohui Huang
To address this challenge, we introduce Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow multi-level instructions with complex constraints.
1 code implementation • 18 Mar 2024 • Yanling Wang, Jing Zhang, Lingxi Zhang, Lixin Liu, Yuxiao Dong, Cuiping Li, Hong Chen, Hongzhi Yin
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.
no code implementations • 11 Aug 2022 • Lixin Liu, Yanling Wang, Tianming Wang, Dong Guan, Jiawei Wu, Jingxu Chen, Rong Xiao, Wenxiang Zhu, Fei Fang
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
no code implementations • CUHK Course IERG5350 2020 • Hanyuan Liu, Lixin Liu
We also applied several state of the art reinforcement learning algorithms such as Dreamer, DrQ, and Plan2Explore in the real-world Tetris game environment.
no code implementations • ICCV 2019 • Lixin Liu, Jiajun Tang, Xiaojun Wan, Zongming Guo
Recently there has been significant progress in image captioning with the help of deep learning.
4 code implementations • NeurIPS 2018 • Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.