5 code implementations • 16 Oct 2023 • Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
no code implementations • 9 Oct 2023 • Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jiameng Liu, Yansu He, Yuan Yuan, Yu Liu, James Zhang, Yujiu Yang, Hao Wang
To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.
1 code implementation • 9 Oct 2023 • Chen Pan, Fan Zhou, Xuanwei Hu, Xinxin Zhu, Wenxin Ning, Zi Zhuang, Siqiao Xue, James Zhang, Yunhua Hu
Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data.
1 code implementation • 10 Aug 2023 • Siqiao Xue, Fan Zhou, Yi Xu, Ming Jin, Qingsong Wen, Hongyan Hao, Qingyang Dai, Caigao Jiang, Hongyu Zhao, Shuo Xie, Jianshan He, James Zhang, Hongyuan Mei
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
no code implementations • 29 Jul 2023 • Hongyan Hao, Zhixuan Chu, Shiyi Zhu, Gangwei Jiang, Yan Wang, Caigao Jiang, James Zhang, Wei Jiang, Siqiao Xue, Jun Zhou
In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.
1 code implementation • 16 Jun 2023 • Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
no code implementations • 16 Jun 2023 • Shuai Xiao, Chen Pan, Min Wang, Xinxin Zhu, Siqiao Xue, Jing Wang, Yunhua Hu, James Zhang, Jinghua Feng
To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business.
1 code implementation • 1 May 2023 • Shiyu Wang, Yinbo Sun, Xiaoming Shi, Shiyi Zhu, Lin-Tao Ma, James Zhang, Yifei Zheng, Jian Liu
The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate.
no code implementations • 11 Feb 2023 • Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
1 code implementation • 28 Dec 2022 • Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Bo Zheng, Lei Lei, Yun Hu
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
no code implementations • 21 Nov 2022 • Siqiao Xue, Xiaoming Shi, Hongyan Hao, Lintao Ma, Shiyu Wang, Shijun Wang, James Zhang
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals.
no code implementations • 6 Oct 2022 • Xiong Junwu, Xiaoyun Feng, Yunzhou Shi, James Zhang, Zhongzhou Zhao, Wei Zhou
Our proposed framework learns through real-time interactions between the digital human and customers dynamically through the state-of-art RL algorithms, combined with multimodal embedding and graph embedding, to improve the accuracy of personalization and thus enable the digital human agent to timely catch the attention of the customer.
no code implementations • 11 Jul 2022 • Caigao Jiang, Siqiao Xue, James Zhang, Lingyue Liu, Zhibo Zhu, Hongyan Hao
However, unlike natural language processing (NLP) tasks, the parameters of user behaviour model come mostly from user embedding layer, which makes most existing works fail in training a universal user embedding of large scale.
1 code implementation • 31 May 2022 • Siqiao Xue, Chao Qu, Xiaoming Shi, Cong Liao, Shiyi Zhu, Xiaoyu Tan, Lintao Ma, Shiyu Wang, Shijun Wang, Yun Hu, Lei Lei, Yangfei Zheng, Jianguo Li, James Zhang
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
1 code implementation • 29 Jan 2022 • Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
1 code implementation • NeurIPS 2021 • Huiru Xiao, Caigao Jiang, Yangqiu Song, James Zhang, Junwu Xiong
Specifically, we propose to learn the embeddings of hierarchically structured data in the unit ball model of the complex hyperbolic space.
no code implementations • 16 Jun 2020 • Xiaoyu Tan, Chao Qu, Junwu Xiong, James Zhang
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL).
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 9 Jun 2020 • Baocheng Zhu, Shijun Wang, James Zhang
In this paper, by leveraging recent progresses in representation learning and state space models (SSMs), we propose NeurPhy, which uses variational auto-encoder (VAE) to extract underlying Markovian dynamic state at each time step, neural process (NP) to extract the global system parameters, and a non-linear non-recurrent stochastic state space model to learn the physical dynamic transition.
no code implementations • 19 May 2020 • Shijun Wang, Baocheng Zhu, Chen Li, Mingzhe Wu, James Zhang, Wei Chu, Yuan Qi
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems.
no code implementations • 19 Apr 2020 • Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 25 Sep 2019 • Xiaoyu Tan, Chao Qu, Junwu Xiong, James Zhang
In this paper, we propose a simple and elegant model-based reinforcement learning algorithm called soft stochastic value gradient method (S2VG).
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 30 Jan 2018 • James Zhang, Ilija Vukotic, Robert Gardner
Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset.