Search Results for author: Bo Pan

Found 17 papers, 1 papers with code

TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations

no code implementations27 May 2024 Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, Liang Zhao

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios.

Representation Learning Self-Supervised Learning +1

GRAG: Graph Retrieval-Augmented Generation

no code implementations26 May 2024 Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, Liang Zhao

To address this challenge, we introduce $\textbf{Graph Retrieval-Augmented Generation (GRAG)}$, which significantly enhances both the retrieval and generation processes by emphasizing the importance of subgraph structures.

Entity Retrieval Retrieval

Deep Causal Generative Models with Property Control

no code implementations25 May 2024 Qilong Zhao, Shiyu Wang, Guangji Bai, Bo Pan, Zhaohui Qin, Liang Zhao

This is due to the long-lasting challenge of jointly identifying key latent variables, their causal relations, and their correlation with properties of interest, as well as how to leverage their discoveries toward causally controlled data generation.

ELAD: Explanation-Guided Large Language Models Active Distillation

no code implementations20 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.

Active Learning Knowledge Distillation

Distilling Large Language Models for Text-Attributed Graph Learning

no code implementations19 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.

Graph Learning TAG

AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems

no code implementations14 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.

Language Modelling Large Language Model

Explaining latent representations of generative models with large multimodal models

no code implementations2 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.

Disentanglement Explanation Generation

XAI Benchmark for Visual Explanation

no code implementations12 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.

Decision Making Explainable artificial intelligence +2

Visual Attention Prompted Prediction and Learning

1 code implementation12 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.

Decision Making

SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation

no code implementations11 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.

Decision Making

Controllable Data Generation Via Iterative Data-Property Mutual Mappings

no code implementations11 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.

Disentanglement

XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis

no code implementations25 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.

Data Visualization

MAGI: Multi-Annotated Explanation-Guided Learning

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.

Variational Inference

Multi-objective Deep Data Generation with Correlated Property Control

no code implementations1 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.

Image Generation

Controllable Data Generation by Deep Learning: A Review

no code implementations19 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.

Speech Synthesis

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

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.

reinforcement-learning Reinforcement Learning (RL)

An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization

no code implementations28 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.

Data Augmentation

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