no code implementations • 11 Mar 2024 • Chi-Yang Hsu, Kyle Cox, Jiawei Xu, Zhen Tan, Tianhua Zhai, Mengzhou Hu, Dexter Pratt, Tianlong Chen, Ziniu Hu, Ying Ding
We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes.
no code implementations • 2 Mar 2024 • Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A. Ross, Cordelia Schmid, Alireza Fathi
SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene.
1 code implementation • 22 Feb 2024 • Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
1 code implementation • 7 Feb 2024 • Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Kai Shu, Adel Bibi, Ziniu Hu, Philip Torr, Bernard Ghanem, Guohao Li
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
1 code implementation • 15 Jan 2024 • Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, Jie Tang
To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.
no code implementations • 10 Oct 2023 • Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, Wei Wang
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.
1 code implementation • 8 Oct 2023 • Jonathan Light, Min Cai, Sheng Shen, Ziniu Hu
In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon.
1 code implementation • 20 Jul 2023 • Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
no code implementations • 24 Jun 2023 • Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Yingyan, Lin, Jason Cong, Yizhou Sun
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
no code implementations • 7 Jun 2023 • Xiusi Chen, Wei-Yao Wang, Ziniu Hu, Curtis Chou, Lam Hoang, Kun Jin, Mingyan Liu, P. Jeffrey Brantingham, Wei Wang
To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.
no code implementations • 18 May 2023 • Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin, Ziniu Hu, Yizhou Sun, Jason Cong
In addition, these programs can be compiled and converted into a control data flow graph (CDFG), and the compiler also provides fine-grained alignment between the code tokens and the CDFG nodes.
1 code implementation • CVPR 2023 • Ziniu Hu, Ahmet Iscen, Chen Sun, ZiRui Wang, Kai-Wei Chang, Yizhou Sun, Cordelia Schmid, David A. Ross, Alireza Fathi
REVEAL consists of four key components: the memory, the encoder, the retriever and the generator.
Ranked #9 on Visual Question Answering (VQA) on OK-VQA
no code implementations • 15 Nov 2022 • Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, ZiYi Yang, Chenguang Zhu, Kai-Wei Chang, Yizhou Sun
Answering open-domain questions requires world knowledge about in-context entities.
no code implementations • 19 May 2022 • Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi
First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.
1 code implementation • 3 Mar 2022 • Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.
no code implementations • Findings (EMNLP) 2021 • Ziniu Hu, Yizhou Sun, Kai-Wei Chang
Answering complex open-domain questions requires understanding the latent relations between involving entities.
1 code implementation • EMNLP 2021 • Da Yin, Liunian Harold Li, Ziniu Hu, Nanyun Peng, Kai-Wei Chang
Commonsense is defined as the knowledge that is shared by everyone.
Ranked #1 on Visual Commonsense Reasoning on GD-VCR
Cultural Vocal Bursts Intensity Prediction Visual Commonsense Reasoning
1 code implementation • 5 Aug 2021 • Xuelu Chen, Ziniu Hu, Yizhou Sun
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task.
no code implementations • 23 Dec 2020 • Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
2 code implementations • 27 Jun 2020 • Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
no code implementations • ICLR 2020 • Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
4 code implementations • 3 Mar 2020 • Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Ranked #25 on Node Property Prediction on ogbn-mag
1 code implementation • NeurIPS 2019 • Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.
no code implementations • 25 Sep 2019 • Yewen Wang, Ziniu Hu, Yusong Ye, Yizhou Sun
However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.
no code implementations • 25 Sep 2019 • Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun
Besides, the understanding of relationships among tasks has been ignored by most of the current methods.
1 code implementation • ACL 2019 • Ziniu Hu, Ting Chen, Kai-Wei Chang, Yizhou Sun
Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.
no code implementations • 31 May 2019 • Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun
With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.
1 code implementation • 16 Sep 2018 • Ziniu Hu, Yang Wang, Qu Peng, Hang Li
Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i. e., learning-to-rank.
1 code implementation • 7 Jun 2018 • Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
4 code implementations • 6 Dec 2017 • Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
Ranked #16 on Stock Market Prediction on Astock