no code implementations • 20 Feb 2024 • Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
no code implementations • 19 Feb 2024 • Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao
To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.
no code implementations • 14 Feb 2024 • Jiaying Lu, Bo Pan, Jieyi Chen, Yingchaojie Feng, Jingyuan Hu, Yuchen Peng, Wei Chen
Recently, Large Language Model based Autonomous system(LLMAS) has gained great popularity for its potential to simulate complicated behaviors of human societies.
no code implementations • 2 Feb 2024 • Mengdan Zhu, Zhenke Liu, Bo Pan, Abhinav Angirekula, Liang Zhao
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.
no code implementations • 12 Oct 2023 • Yifei Zhang, Siyi Gu, James Song, Bo Pan, Guangji Bai, Liang Zhao
Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.
1 code implementation • 12 Oct 2023 • Yifei Zhang, Siyi Gu, Bo Pan, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao
However, in many real-world situations, it is usually desired to prompt the model with visual attention without model retraining.
no code implementations • 11 Oct 2023 • Bo Pan, Zhenke Liu, Yifei Zhang, Liang Zhao
Explainable AI seeks to bring light to the decision-making processes of black-box models.
no code implementations • 11 Oct 2023 • Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao
To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.
no code implementations • 25 Jan 2023 • Yingchaojie Feng, Xingbo Wang, Bo Pan, Kam Kwai Wong, Yi Ren, Shi Liu, Zihan Yan, Yuxin Ma, Huamin Qu, Wei Chen
Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.
no code implementations • ICCV 2023 • Yifei Zhang, Siyi Gu, Yuyang Gao, Bo Pan, Xiaofeng Yang, Liang Zhao
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
no code implementations • 1 Oct 2022 • Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William Wuest, Amarda Shehu, Liang Zhao
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
no code implementations • 19 Jul 2022 • Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
no code implementations • NeurIPS 2021 • Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.
no code implementations • 28 Jul 2021 • Fan Zhang, Bo Pan, Pengfei Shao, Peng Liu, Shuwei Shen, Peng Yao, Ronald X. Xu
In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions related to the disease from sMRI data.