no code implementations • 26 Oct 2023 • Junfeng Hu, Xu Liu, Zhencheng Fan, Yuxuan Liang, Roger Zimmermann
Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework.
1 code implementation • 15 Oct 2023 • Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, Roger Zimmermann
To address these issues, we propose UniTime for effective cross-domain time series learning.
1 code implementation • NeurIPS 2023 • Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann
To mitigate these limitations, we introduce the LargeST benchmark dataset.
1 code implementation • 30 May 2023 • Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Hongyang Chen, Yu Zheng, Roger Zimmermann
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph.
no code implementations • 16 Sep 2021 • Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Li Liu, Yifang Yin, Roger Zimmermann
Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns.
no code implementations • 3 Mar 2021 • Ping Gong, Wenwen Yu, Qiuwen Sun, Ruohan Zhao, Junfeng Hu
Specifically, our approach consists of two key modules, a conditional domain discriminator~(CDD) and a category-centric prototype aligner~(CCPA).
no code implementations • WMT (EMNLP) 2020 • Jin Xu, Yinuo Guo, Junfeng Hu
Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence.
no code implementations • 30 Sep 2020 • Junfeng Hu, Xiaosa Li, Yuru Xu, Shaowu Wu, Bin Zheng
In this paper, company investment value evaluation models are established based on comprehensive company information.
1 code implementation • 6 Nov 2019 • Junfeng Hu, Zhencheng Fan, Jun Liao, Li Liu
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
no code implementations • WS 2019 • Yinuo Guo, Junfeng Hu
This paper describes Meteor++ 2. 0, our submission to the WMT19 Metric Shared Task.
no code implementations • WS 2018 • Yuqi Sun, Haoyue Shi, Junfeng Hu
In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors.
no code implementations • WS 2018 • Yinuo Guo, Chong Ruan, Junfeng Hu
In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference.
no code implementations • 3 Mar 2018 • Haoyue Shi, Yuqi Sun, Junfeng Hu
Unsupervised learned representations of polysemous words generate a large of pseudo multi senses since unsupervised methods are overly sensitive to contextual variations.
no code implementations • 5 Jul 2017 • Zhaocheng Zhu, Junfeng Hu
Recently, doc2vec has achieved excellent results in different tasks.
no code implementations • 25 May 2017 • Zhipeng Xie, Junfeng Hu
Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications.
no code implementations • WS 2016 • Haoyue Shi, Caihua Li, Junfeng Hu
Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks.
no code implementations • LREC 2016 • Liumingjing Xiao, Chong Ruan, An Yang, Junhao Zhang, Junfeng Hu
Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field.
no code implementations • LREC 2014 • Shaoda He, Xiaojun Zou, Liumingjing Xiao, Junfeng Hu
Preliminary experiments show that our algorithm outperforms the Google{'}s RNN and K-means based algorithm in both concepts discovery and concepts hierarchical clustering.