2 code implementations • CVPR 2021 • Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.
1 code implementation • CVPR 2023 • Donghao Zhou, Chunbin Gu, Junde Xu, Furui Liu, Qiong Wang, Guangyong Chen, Pheng-Ann Heng
In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures.
1 code implementation • 6 Oct 2020 • Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.
1 code implementation • 3 Mar 2023 • Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng
To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL).
1 code implementation • ICCV 2023 • Juzheng Miao, Cheng Chen, Furui Liu, Hao Wei, Pheng-Ann Heng
Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature.
1 code implementation • NeurIPS 2023 • Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang
To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.
1 code implementation • 21 Oct 2023 • Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang, Jun Wang
Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.
1 code implementation • 23 Aug 2022 • Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).
no code implementations • 21 Mar 2018 • Furui Liu, Laiwan Chan
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain.
no code implementations • 19 Mar 2018 • Furui Liu, Laiwan Chan
Based on an assumption of rotation invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of $X_n$ is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder.
no code implementations • 2 Jul 2020 • Yifei Wang, Dan Peng, Furui Liu, Zhenguo Li, Zhitang Chen, Jiansheng Yang
Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevitably leads to severe accuracy reduction as it discards the non-robust yet useful features.
no code implementations • 1 Jan 2021 • Peng Zhang, Furui Liu, Zhitang Chen, Jianye Hao, Jun Wang
Reinforcement Learning (RL) has shown great potential to deal with sequential decision-making problems.
no code implementations • 28 Dec 2020 • Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang
The capability of imagining internally with a mental model of the world is vitally important for human cognition.
no code implementations • 28 May 2021 • Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang
In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.
no code implementations • 1 Jun 2021 • Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao
Specifically, Shapley Value and its desired properties are leveraged in deep MARL to credit any combinations of agents, which grants us the capability to estimate the individual credit for each agent.
no code implementations • 2 Jun 2021 • Yunqi Wang, Furui Liu, Zhitang Chen, Qing Lian, Shoubo Hu, Jianye Hao, Yik-Chung Wu
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.
no code implementations • 29 Sep 2021 • Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang
In many real-world scenarios, such as image classification and recommender systems, it is evidence that representation learning can improve model's performance over multiple downstream tasks.
no code implementations • 25 Sep 2019 • Tianshuo Cong, Dan Peng, Furui Liu, Zhitang Chen
Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.
no code implementations • 20 Dec 2021 • Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu
The node information compression aims to address the problem of what to communicate via learning compact node representations.
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 16 Jan 2022 • Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
no code implementations • 17 Feb 2022 • Mengyue Yang, Xinyu Cai, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems.
no code implementations • 20 Jun 2022 • Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu
To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.
Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2
no code implementations • 26 Jul 2022 • Zeren Huang, WenHao Chen, Weinan Zhang, Chuhan Shi, Furui Liu, Hui-Ling Zhen, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers.
no code implementations • 28 Nov 2022 • Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang
e. g., an agent is a random policy while other agents are medium policies.
no code implementations • 4 Dec 2022 • Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu
The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet.
no code implementations • 6 Mar 2023 • Bowen Wang, Chen Liang, Jiaze Wang, Furui Liu, Shaogang Hao, Dong Li, Jianye Hao, Guangyong Chen, Xiaolong Zou, Pheng-Ann Heng
Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm.
Initial Structure to Relaxed Energy (IS2RE), Direct Property Prediction
no code implementations • ICCV 2023 • Hao Chen, Jiaze Wang, Kun Shao, Furui Liu, Jianye Hao, Chenyong Guan, Guangyong Chen, Pheng-Ann Heng
Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities.
1 code implementation • 21 Mar 2023 • Yang Yu, Danruo Deng, Furui Liu, Yueming Jin, Qi Dou, Guangyong Chen, Pheng-Ann Heng
Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers).
no code implementations • 25 May 2023 • Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu, Xingxuan Zhang, Linjun Zhou, Furui Liu
Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.
no code implementations • 27 Sep 2023 • Hao Wei, Peilun Shi, Juzheng Miao, Minqing Zhang, Guitao Bai, Jianing Qiu, Furui Liu, Wu Yuan
Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics.
no code implementations • 21 Dec 2023 • Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao
By considering the underappreciated causal generating process, first, we pinpoint the source of the vulnerability of DNNs via the lens of causality, then give theoretical results to answer \emph{where to attack}.