no code implementations • ACL (ECNLP) 2021 • Haoran Shi, Zhibiao Rao, Yongning Wu, Zuohua Zhang, Chu Wang
In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists.
1 code implementation • 8 May 2024 • Yuhang Wu, Yingfei Wang, Chu Wang, Zeyu Zheng
Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios and generating output given specific instructions and multimodal input.
no code implementations • 5 Mar 2024 • Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha, Qi Zhou
Metric vectors are regarded as located on latent uniform domain, wherein spatial and spectral transformation offer highly regular constraints on solution space.
no code implementations • 2 Mar 2024 • Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha, Qi Zhou
The framework is generalized to deal with new parametric conditions of systems.
no code implementations • 9 Feb 2023 • Xiaoibin Wang, Shuang Gao, Yuntao Zou, Jianlan Guo, Chu Wang
For the problems of low recognition rate and slow recognition speed of traditional detection methods in IC appearance defect detection, we propose an IC appearance defect detection algo-rithm IH-ViT.
no code implementations • 3 Feb 2023 • Chu Wang, Manfeng Dou, Zhongliang Li, Rachid Outbib, Dongdong Zhao, Jian Zuo, Yuanlin Wang, Bin Liang, Peng Wang
Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC).
no code implementations • ACL 2021 • Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok Popat, Tomas Pfister
Natural reading orders of words are crucial for information extraction from form-like documents.
no code implementations • CVPR 2020 • Chu Wang, Babak Samari, Vladimir G. Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
Affinity graphs are widely used in deep architectures, including graph convolutional neural networks and attention networks.
1 code implementation • 4 Jun 2019 • Chu Wang, Marcello Pelillo, Kaleem Siddiqi
We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets.
no code implementations • 27 May 2019 • Chu Wang, Babak Samari, Vladimir Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
Thus far the learning of attention weights has been driven solely by the minimization of task specific loss functions.
no code implementations • 18 Apr 2019 • Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu
ProductNet is a collection of high-quality product datasets for better product understanding.
no code implementations • 11 Apr 2019 • Chu Wang, Lei Tang, Shujun Bian, Da Zhang, Zuohua Zhang, Yongning Wu
For a product of interest, we propose a search method to surface a set of reference products.
no code implementations • 1 Aug 2018 • Chu Wang, Yan-Ming Zhang, Cheng-Lin Liu
Anomaly detection aims to detect abnormal events by a model of normality.
1 code implementation • ECCV 2018 • Chu Wang, Babak Samari, Kaleem Siddiqi
In the present article, we propose to overcome this limitation by using spectral graph convolution on a local graph, combined with a novel graph pooling strategy.
Ranked #82 on 3D Point Cloud Classification on ModelNet40
no code implementations • 13 Sep 2017 • Yingfei Wang, Chu Wang, Warren Powell
We also show that the knowledge gradient policy is asymptotically optimal in an offline setting.
no code implementations • 21 Jul 2017 • Chu Wang, Iraj Saniee, William S. Kennedy, Chris A. White
We show that for structured data including categorical and continuous data, the near-metrics corresponding to normalized forward k-step diffusion (k small) work as one of the best performing similarity measures; for vector representations of text and images including those extracted from deep learning, the near-metrics derived from normalized and reverse k-step graph diffusion (k very small) exhibit outstanding ability to distinguish data points from different classes.
no code implementations • 13 Sep 2016 • Bernard Chazelle, Chu Wang
An important result from psycholinguistics (Griffiths & Kalish, 2005) states that no language can be learned iteratively by rational agents in a self-sustaining manner.
no code implementations • 9 Oct 2015 • Chu Wang, Yingfei Wang, Weinan E, Robert Schapire
Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance.
no code implementations • 8 Oct 2015 • Yingfei Wang, Chu Wang, Warren Powell
We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives.