Search Results for author: Shiyu Wang

Found 29 papers, 11 papers with code

Deep Causal Generative Models with Property Control

no code implementations25 May 2024 Qilong Zhao, Shiyu Wang, Guangji Bai, Bo Pan, Zhaohui Qin, Liang Zhao

This is due to the long-lasting challenge of jointly identifying key latent variables, their causal relations, and their correlation with properties of interest, as well as how to leverage their discoveries toward causally controlled data generation.

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

1 code implementation ICLR 2024 Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou

Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations in a novel view of multiscale-mixing, which is based on an intuitive but important observation that time series present distinct patterns in different sampling scales.

Future prediction Time Series +2

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

1 code implementation7 May 2024 DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Yang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, JianZhong Guo, Jiaqi Ni, Jiashi Li, Jin Chen, Jingyang Yuan, Junjie Qiu, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruizhe Pan, Runxin Xu, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Size Zheng, T. Wang, Tian Pei, Tian Yuan, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Liu, Xin Xie, Xingkai Yu, Xinnan Song, Xinyi Zhou, Xinyu Yang, Xuan Lu, Xuecheng Su, Y. Wu, Y. K. Li, Y. X. Wei, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Zheng, Yichao Zhang, Yiliang Xiong, Yilong Zhao, Ying He, Ying Tang, Yishi Piao, Yixin Dong, Yixuan Tan, Yiyuan Liu, Yongji Wang, Yongqiang Guo, Yuchen Zhu, Yuduan Wang, Yuheng Zou, Yukun Zha, Yunxian Ma, Yuting Yan, Yuxiang You, Yuxuan Liu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhewen Hao, Zhihong Shao, Zhiniu Wen, Zhipeng Xu, Zhongyu Zhang, Zhuoshu Li, Zihan Wang, Zihui Gu, Zilin Li, Ziwei Xie

MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.

Language Modelling Reinforcement Learning (RL)

Gene-associated Disease Discovery Powered by Large Language Models

no code implementations16 Jan 2024 Jiayu Chang, Shiyu Wang, Chen Ling, Zhaohui Qin, Liang Zhao

The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases.

Decision Making Retrieval

Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

1 code implementation1 Jan 2024 Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao

We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.

Intelligent Virtual Assistants with LLM-based Process Automation

no code implementations4 Dec 2023 Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Longfei Li, Jinjie Gu, Chenyi Zhuang

This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests.

Language Modelling Large Language Model

Leveraging sinusoidal representation networks to predict fMRI signals from EEG

no code implementations6 Nov 2023 Yamin Li, Ange Lou, Ziyuan Xu, Shiyu Wang, Catie Chang

The ability to obtain fMRI information from EEG would enable cost-effective, imaging across a wider set of brain regions.

EEG Feature Engineering

TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds

no code implementations16 Oct 2023 Jingkai Yan, Shiyu Wang, Xinyu Rain Wei, Jimmy Wang, Zsuzsanna Márka, Szabolcs Márka, John Wright

In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability.

Computational Efficiency Gravitational Wave Detection +1

Controllable Data Generation Via Iterative Data-Property Mutual Mappings

no code implementations11 Oct 2023 Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao

To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.

Disentanglement

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

7 code implementations10 Oct 2023 Yong liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long

These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.

Time Series Time Series Forecasting

Domain Generalization Deep Graph Transformation

no code implementations19 May 2023 Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao

As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.

Decoder Domain Generalization +2

Full Scaling Automation for Sustainable Development of Green Data Centers

1 code implementation1 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.

Cloud Computing Representation Learning

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

no code implementations11 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.

Decision Making Multivariate Time Series Forecasting +1

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

1 code implementation28 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.

Multivariate Time Series Forecasting Time Series

A Graph Regularized Point Process Model For Event Propagation Sequence

no code implementations21 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.

Multi-objective Deep Data Generation with Correlated Property Control

no code implementations1 Oct 2022 Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William Wuest, Amarda Shehu, Liang Zhao

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.

Image Generation

Controllable Data Generation by Deep Learning: A Review

no code implementations19 Jul 2022 Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao

This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.

Speech Synthesis

A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

1 code implementation31 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.

Decision Making Management +3

Sample Recycling for Nested Simulation with Application in Portfolio Risk Measurement

no code implementations29 Mar 2022 Kun Zhang, Ben Mingbin Feng, Guangwu Liu, Shiyu Wang

The resulting sample of conditional expectations is then used to estimate different risk measures of interest.

valid

Deep Generative Model for Periodic Graphs

1 code implementation28 Jan 2022 Shiyu Wang, Xiaojie Guo, Liang Zhao

To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative models for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns.

Decoder

Global Regular Network for Writer Identification

no code implementations16 Jan 2022 Shiyu Wang

The proposed GRN has two attributions: one is adding a branch to extract features contained in page; the other is using residual attention network to extract local feature.

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

1 code implementation NeurIPS Workshop AI4Scien 2021 Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao

Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.

BIG-bench Machine Learning Graph Generation +1

Quantum walks on a programmable two-dimensional 62-qubit superconducting processor

no code implementations4 Feb 2021 Ming Gong, Shiyu Wang, Chen Zha, Ming-Cheng Chen, He-Liang Huang, Yulin Wu, Qingling Zhu, YouWei Zhao, Shaowei Li, Shaojun Guo, Haoran Qian, Yangsen Ye, Fusheng Chen, Jiale Yu, Daojing Fan, Dachao Wu, Hong Su, Hui Deng, Hao Rong, Jin Lin, Yu Xu, Lihua Sun, Cheng Guo, Futian Liang, Kae Nemoto, W. J. Munro, Chao-Yang Lu, Cheng-Zhi Peng, Xiaobo Zhu, Jian-Wei Pan

Quantum walks are the quantum mechanical analogue of classical random walks and an extremely powerful tool in quantum simulations, quantum search algorithms, and even for universal quantum computing.

Quantum Physics

Experimental characterization of quantum many-body localization transition

no code implementations21 Dec 2020 Ming Gong, Gentil D. de Moraes Neto, Chen Zha, Yulin Wu, Hao Rong, Yangsen Ye, Shaowei Li, Qingling Zhu, Shiyu Wang, YouWei Zhao, Futian Liang, Jin Lin, Yu Xu, Cheng-Zhi Peng, Hui Deng, Abolfazl Bayat, Xiaobo Zhu, Jian-Wei Pan

Here, we experimentally implement a scalable protocol for detecting the many-body localization transition point, using the dynamics of a $N=12$ superconducting qubit array.

Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons

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