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of 10
pro vyhledávání: '"Xiao, Tingxiong"'
Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression. However, fMRI compressing poses unique challenges due to its intricate temporal dynamics, low signal-to
Externí odkaz:
http://arxiv.org/abs/2312.00082
Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performance on real data. This study introduces the CausalTime pipeline to gene
Externí odkaz:
http://arxiv.org/abs/2310.01753
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions. Here we introduce HOPE (High-order Polynomial Ex
Externí odkaz:
http://arxiv.org/abs/2307.08192
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research. Due to its universal approximation property, neural network is widely used to
Externí odkaz:
http://arxiv.org/abs/2305.10033
Autor:
Cheng, Yuxiao, Li, Lianglong, Xiao, Tingxiong, Li, Zongren, Zhong, Qin, Suo, Jinli, He, Kunlun
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Gr
Externí odkaz:
http://arxiv.org/abs/2305.05890
Autor:
Cheng, Yuxiao, Yang, Runzhao, Xiao, Tingxiong, Li, Zongren, Suo, Jinli, He, Kunlun, Dai, Qionghai
Publikováno v:
The Eleventh International Conference on Learning Representations, Feb. 2023
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most exis
Externí odkaz:
http://arxiv.org/abs/2302.07458
Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is no
Externí odkaz:
http://arxiv.org/abs/2211.06689
Autor:
Yang, Runzhao, Xiao, Tingxiong, Cheng, Yuxiao, Cao, Qianni, Qu, Jinyuan, Suo, Jinli, Dai, Qionghai
Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural ima
Externí odkaz:
http://arxiv.org/abs/2209.15180
Akademický článek
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Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence; December 2024, Vol. 46 Issue: 12 p7924-7939, 16p
