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Xiyou Zhou
Researcher at University of California, Santa Barbara
Publications - 12
Citations - 1015
Xiyou Zhou is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Semantics & Language model. The author has an hindex of 6, co-authored 10 publications receiving 400 citations. Previous affiliations of Xiyou Zhou include Fudan University.
Papers
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Proceedings Article
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
TL;DR: First, convolutional self-attention is proposed by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism, and LogSparse Transformer is proposed, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget.
Posted Content
TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen,Hongmin Wang,Jianshu Chen,Yunkai Zhang,Hong Wang,Shiyang Li,Xiyou Zhou,William Yang Wang +7 more
TL;DR: A large-scale dataset with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED is constructed and two different models are designed: Table-BERT and Latent Program Algorithm (LPA).
Proceedings Article
TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen,Hongmin Wang,Jianshu Chen,Yunkai Zhang,Hong Wang,Shiyang Li,Xiyou Zhou,William Yang Wang +7 more
TL;DR: Zhang et al. as mentioned in this paper designed two different models: Table-BERT and Latent Program Algorithm (LPA) to verify whether a textual hypothesis holds based on the given evidence, also known as fact verification.
Posted Content
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting.
TL;DR: In this article, a convolutional self-attention with causal convolution was proposed to improve the accuracy of time series forecasting with fine granularity and strong long-term dependencies.
Proceedings ArticleDOI
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
Zhiyu Chen,Wenhu Chen,Hanwen Zha,Xiyou Zhou,Yunkai Zhang,Sairam Sundaresan,William Yang Wang +6 more
TL;DR: This work forms high-fidelity NLG as generation from logical forms in order to obtain controllable and faithful generations, and presents a new large-scale dataset, Logic2Text, with 10,753 descriptions involving common logic types paired with the underlying logical forms.