no code implementations • 14 Apr 2024 • Xiaoshu Chen, Sihang Zhou, Ke Liang, Xinwang Liu
Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models (LLMs) beyond simply predicting the answer to the question.
1 code implementation • 5 Dec 2023 • Ke Liang, Sifan Wu, Jiayi Gu
Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN.
no code implementations • 28 Nov 2023 • Dayu Hu, Ke Liang, Hao Yu, Xinwang Liu
This model leverages exogenous gene network information to facilitate the clustering process, generating discriminative representations.
1 code implementation • 28 Nov 2023 • Dayu Hu, Zhibin Dong, Ke Liang, Jun Wang, Siwei Wang, Xinwang Liu
To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.
no code implementations • 26 Sep 2023 • Xinhang Wan, Jiyuan Liu, Hao Yu, Ao Li, Xinwang Liu, Ke Liang, Zhibin Dong, En Zhu
Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.
1 code implementation • 21 Sep 2023 • Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, Xinwang Liu
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
1 code implementation • 31 Aug 2023 • Yi Wen, Suyuan Liu, Xinhang Wan, Siwei Wang, Ke Liang, Xinwang Liu, Xihong Yang, Pei Zhang
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
1 code implementation • 31 Aug 2023 • Yi Wen, Siwei Wang, Ke Liang, Weixuan Liang, Xinhang Wan, Xinwang Liu, Suyuan Liu, Jiyuan Liu, En Zhu
Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).
1 code implementation • 17 Aug 2023 • Xihong Yang, Jiaqi Jin, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, En Zhu
Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
2 code implementations • 17 Aug 2023 • Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
2 code implementations • 13 Aug 2023 • Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
1 code implementation • 7 Jul 2023 • Yi Wen, Siwei Wang, Qing Liao, Weixuan Liang, Ke Liang, Xinhang Wan, Xinwang Liu
Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.
no code implementations • 6 Jul 2023 • Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, Xinwang Liu
To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.
no code implementations • 8 Jun 2023 • Meng Liu, Ke Liang, Yue Liu, Siwei Wang, Sihang Zhou, Xinwang Liu
It makes evaluating models for large-scale temporal graph clustering challenging.
3 code implementations • 28 May 2023 • Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li
Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.
no code implementations • 23 May 2023 • Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
1 code implementation • 18 May 2023 • Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu
To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.
no code implementations • 20 Apr 2023 • Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu
Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.
no code implementations • 6 Mar 2023 • Yulin He, Wei Chen, Ke Liang, Yusong Tan, Zhengfa Liang, Yulan Guo
Our proposed method, Pseudo-label Correction and Learning (PCL), is extensively evaluated on the MS COCO and PASCAL VOC benchmarks.
no code implementations • 15 Feb 2023 • Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinbiao Gan, Xinwang Liu, Kunlun He
To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.
2 code implementations • 16 Dec 2022 • Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
1 code implementation • 12 Dec 2022 • Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.
1 code implementation • 7 Dec 2022 • Xihong Yang, Yue Liu, Ke Liang, Sihang Zhou, Xinwang Liu, En Zhu
To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC.
2 code implementations • 23 Nov 2022 • Yue Liu, Jun Xia, Sihang Zhou, Xihong Yang, Ke Liang, Chenchen Fan, Yan Zhuang, Stan Z. Li, Xinwang Liu, Kunlun He
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
no code implementations • 19 Nov 2022 • Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu
To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.
no code implementations • 5 Apr 2021 • Ke Liang, Mitchel Myers
We surveyed both centralized and decentralized ML routing architectures and using a variety of ML techniques broadly divided into supervised learning and reinforcement learning.
no code implementations • 12 Dec 2018 • Ke Liang
We present Fission, a new permissionless blockchain that achieves scalability in both terms of system throughput and transaction confirmation time, while at the same time, retaining blockchain's core values of equality and decentralization.
Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture