Author name cluster

Xianggen Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

AAAI Conference 2024 Conference Paper

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting

Wanlin Cai
Yuxuan Liang
Xianggen Liu
Jianshuai Feng
Yuankai Wu

Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-series correlations across different time scales among multiple time series, an area that has received limited attention in the literature. To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution. By leveraging frequency domain analysis, MSGNet effectively extracts salient periodic patterns and decomposes the time series into distinct time scales. The model incorporates a self-attention mechanism to capture intra-series dependencies, while introducing an adaptive mixhop graph convolution layer to autonomously learn diverse inter-series correlations within each time scale. Extensive experiments are conducted on several real-world datasets to showcase the effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to automatically learn explainable multi-scale inter-series correlations, exhibiting strong generalization capabilities even when applied to out-of-distribution samples.

PDF Details DOI

IJCAI Conference 2022 Conference Paper

Abstract Rule Learning for Paraphrase Generation

Xianggen Liu
Wenqiang Lei
Jiancheng Lv
Jizhe Zhou

In early years, paraphrase generation typically adopts rule-based methods, which are interpretable and able to make global transformations to the original sentence. But they struggle to produce fluent paraphrases. Recently, deep neural networks have shown impressive performances in generating paraphrases. However, the current neural models are black boxes and are prone to make local modifications to the inputs. In this work, we combine these two approaches into RULER, a novel approach that performs abstract rule learning for paraphrasing. The key idea is to explicitly learn generalizable rules that could enhance the paraphrase generation process of neural networks. In RULER, we first propose a rule generalizability metric to guide the model to generate rules underlying the paraphrasing. Then, we leverage neural networks to generate paraphrases by refining the sentences transformed by the learned rules. Extensive experimental results demonstrate the superiority of RULER over previous state-of-the-art methods in terms of paraphrase quality, generalization ability and interpretability.

PDF Details DOI

NeurIPS Conference 2022 Conference Paper

Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences

Wenbo Zhang
likai tang
Site Mo
Xianggen Liu
Sen Song

Abstract reasoning, as one of the hallmarks of human intelligence, involves collecting information, identifying abstract rules, and applying the rules to solve new problems. Although neural networks have achieved human-level performances in several tasks, the abstract reasoning techniques still far lag behind due to the complexity of learning and applying the logic rules, especially in an unsupervised manner. In this work, we propose a novel framework, ARII, that learns rule representations for Abstract Reasoning via Internal Inferences. The key idea is to repeatedly apply a rule to different instances in hope of having a comprehensive understanding (i. e. , representations) of the rule. Specifically, ARII consists of a rule encoder, a reasoner, and an internal referrer. Based on the representations produced by the rule encoder, the reasoner draws the conclusion while the referrer performs internal inferences to regularize rule representations to be robust and generalizable. We evaluate ARII on two benchmark datasets, including PGM and I-RAVEN. We observe that ARII achieves new state-of-the-art records on the majority of the reasoning tasks, including most of the generalization tests in PGM. Our codes are available at https: //github. com/Zhangwenbo0324/ARII.

PDF Details

IJCAI Conference 2021 Conference Paper

Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks

Pengyong Li
Jun Wang
Ziliang Li
Yixuan Qiao
Xianggen Liu
Fei Ma
Peng Gao
Sen Song

Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies. Moreover, the visualization of learned representation revealed that PHD strategy indeed empowers the model to learn graph-level knowledge like the molecular scaffold. These results have established PHD as a powerful and effective self-supervised learning strategy in graph-level representation learning.

PDF Details DOI

ICML Conference 2020 Conference Paper

A Chance-Constrained Generative Framework for Sequence Optimization

Xianggen Liu
Qiang Liu 0001
Sen Song
Jian Peng 0001

Deep generative modeling has achieved many successes for continuous data generation, such as producing realistic images and controlling their properties (e. g. , styles). However, the development of generative modeling techniques for optimizing discrete data, such as sequences or strings, still lags behind largely due to the challenges in modeling complex and long-range constraints, including both syntax and semantics, in discrete structures. In this paper, we formulate the sequence optimization task as a chance-constrained optimization problem. The key idea is to enforce a high probability of generating valid sequences and also optimize the property of interest. We propose a novel minimax algorithm to simultaneously tighten a bound of the valid chance and optimize the expected property. Extensive experimental results in three domains demonstrate the superiority of our approach over the existing sequence optimization methods.

Details

IJCAI Conference 2018 Conference Paper

Jumper: Learning When to Make Classification Decision in Reading

Xianggen Liu
Lili Mou
Haotian Cui
Zhengdong Lu
Sen Song

In early years, text classification is typically accomplished by feature-based classifiers; recently, neural networks, as powerful classifiers, make it possible to work with raw input as the text stands. In this paper, we propose a novel framework, Jumper, inspired by the cognitive process of text reading, that models text classification as a sequential decision process. Basically, Jumper is a neural system that can scan a piece of text sequentially and make classification decision at the time it chooses. Both the classification and when to make the classification are part of the decision process which are controlled by the policy net and trained with reinforcement learning to maximize the overall classification accuracy. Experimental results show that a properly trained Jumper has the following properties: (1) It can make decisions whenever the evidence is enough, therefore reducing the total text reading by 30~40% and often finding the key rationale of prediction. (2) It can achieve classification accuracy better or comparable to state-of-the-art model in several benchmark and industrial datasets.

PDF Details

ECAI Conference 2014 Conference Paper

Semantical Information Graph Model toward Fast Information Valuation in Large Teamwork

Yulin Zhang 0001
Yang Xu 0003
Haixiao Hu
Xianggen Liu

Sharing information is critical to large teamwork for cooperative decision making in dynamic and partially observable environments. To be effective, other than building a full information coverage, agents should valuate how a potential receiver could be benefited with a piece of given information. In this paper, we propose a fast valuation model with complex network based graph modeling and analysis, which help to indicate the information importance to a given information base. Similar to vague information valuation by humans, the key is that important information always significantly changes their complex information graph with its incorporation. Therefore, we calculate the semantic based value of this new information in a graph model and build a local graph evaluation algorithm to estimate information graph evolution, instead of performing expensive complete graph search. Although the decision may be not precise, similar to human communication, it is good enough to disseminate valuable information around the team.

Details