no code implementations • 23 May 2024 • Jingnan Zheng, Han Wang, An Zhang, Tai D. Nguyen, Jun Sun, Tat-Seng Chua
Systematic analysis also validates that the generated test scenarios represent meaningful use cases, as well as integrate enhanced measures to probe long-tail risks.
1 code implementation • 23 May 2024 • Zhiyuan Liu, Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua
To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction.
1 code implementation • 21 May 2024 • Zhiyuan Liu, An Zhang, Hao Fei, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua
ProtT3 empowers an LM to understand protein sequences of amino acids by incorporating a PLM as its protein understanding module, enabling effective protein-to-text generation.
1 code implementation • 21 Feb 2024 • An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang
However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph.
no code implementations • 3 Dec 2023 • Yang Deng, Zifeng Ren, An Zhang, Wenqiang Lei, Tat-Seng Chua
In this work, we investigate a new task, named Goal-oriented Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
2 code implementations • 31 Oct 2023 • Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, Xiangnan He
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.
1 code implementation • NeurIPS 2023 • An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, Tat-Seng Chua
To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods.
1 code implementation • NeurIPS 2023 • Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua
Our results show that a subgraph-level tokenizer and a sufficiently expressive decoder with remask decoding have a large impact on the encoder's representation learning.
1 code implementation • 20 Oct 2023 • Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang
Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process.
1 code implementation • 16 Oct 2023 • An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, Tat-Seng Chua
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development.
1 code implementation • 16 Oct 2023 • An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-Seng Chua
The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i. e., when popularity distribution of test data shifts w. r. t.
1 code implementation • 8 Aug 2023 • Wei Ji, Xiangyan Liu, An Zhang, Yinwei Wei, Yongxin Ni, Xiang Wang
To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features.
no code implementations • 7 Aug 2023 • Yicong Li, Xun Yang, An Zhang, Chun Feng, Xiang Wang, Tat-Seng Chua
This paper identifies two kinds of redundancy in the current VideoQA paradigm.
1 code implementation • 5 Jun 2023 • Fangfu Liu, Wenchang Ma, An Zhang, Xiang Wang, Yueqi Duan, Tat-Seng Chua
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
1 code implementation • 6 Mar 2023 • An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
1 code implementation • 10 Feb 2023 • An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, Tat-Seng Chua
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.
1 code implementation • 20 Oct 2022 • An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.
1 code implementation • 17 Jun 2022 • Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Song Bo, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds.
1 code implementation • 16 Jun 2022 • Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
1 code implementation • 1 Jun 2022 • Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua
Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.
no code implementations • 31 May 2022 • Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
1 code implementation • 23 Apr 2022 • Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
1 code implementation • ICLR 2022 • Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
no code implementations • 21 Jan 2022 • Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
1 code implementation • NeurIPS 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
no code implementations • 12 Apr 2021 • An Zhang, Xiang Wang, Chengfang Fang, Jie Shi, Tat-Seng Chua, Zehua Chen
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
no code implementations • 1 Jan 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
2 code implementations • 3 Jul 2020 • Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.