1 code implementation • 22 Apr 2024 • Hansi Zeng, Chen Luo, Hamed Zamani
This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding.
no code implementations • 27 Mar 2024 • Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo, Qingyu Yin, Ruirui Li, Zheng Li, Wei Wang
Such a constitution discovery pipeline can be run iteratively and automatically to discover new constitutions that specifically target the alignment gaps in the current LLM.
no code implementations • 21 Dec 2023 • Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song
In this paper, we introduce the task of logical session complex query answering, where sessions are treated as hyperedges of items, and we formulate the problem of complex intention understanding as a task of logical session complex queries answering (LS-CQA) on an aggregated hypergraph of sessions, items, and attributes.
no code implementations • 24 Nov 2023 • Taofeng Xie, Zhuo-Xu Cui, Chen Luo, Huayu Wang, Congcong Liu, Yuanzhi Zhang, Xuemei Wang, Yanjie Zhu, Qiyu Jin, Guoqing Chen, Yihang Zhou, Dong Liang, Haifeng Wang
The complementary information can contribute to image reconstruction.
2 code implementations • 15 Nov 2023 • Hansi Zeng, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, Hamed Zamani
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
no code implementations • 24 Sep 2023 • Chen Luo, Huayu Wang, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang
However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications.
no code implementations • 17 Sep 2023 • Huayu Wang, Chen Luo, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang
We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold.
no code implementations • 29 Aug 2023 • Gaurav Gupta, Jonah Yi, Benjamin Coleman, Chen Luo, Vihan Lakshman, Anshumali Shrivastava
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest.
2 code implementations • ICCV 2023 • Mingkai Zheng, Shan You, Lang Huang, Chen Luo, Fei Wang, Chen Qian, Chang Xu
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor.
1 code implementation • NeurIPS 2023 • Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Xiao Cheng, Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun, Jiliang Tang, Bing Yin, Xianfeng Tang
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
1 code implementation • 2 Jun 2023 • Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures.
1 code implementation • NeurIPS 2023 • Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, Yangqiu Song
Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs.
no code implementations • 26 May 2023 • Tao Yang, Cuize Han, Chen Luo, Parth Gupta, Jeff M. Phillips, Qingyao Ai
While previous studies have demonstrated the effectiveness of using user behavior signals (e. g., clicks) as both features and labels of LTR algorithms, we argue that existing LTR algorithms that indiscriminately treat behavior and non-behavior signals in input features could lead to suboptimal performance in practice.
no code implementations • 26 May 2023 • Benjamin Coleman, David Torres Ramos, Vihan Lakshman, Chen Luo, Anshumali Shrivastava
Lookup tables are a fundamental structure in many data processing and systems applications.
no code implementations • 8 Oct 2022 • Haoming Jiang, Tianyu Cao, Zheng Li, Chen Luo, Xianfeng Tang, Qingyu Yin, Danqing Zhang, Rahul Goutam, Bing Yin
When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed.
no code implementations • NAACL 2022 • Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang
User sessions empower many search and recommendation tasks on a daily basis.
no code implementations • 6 Oct 2021 • Nan Jiang, Chen Luo, Vihan Lakshman, Yesh Dattatreya, Yexiang Xue
In addition, FLAN does not require any annotated data or supervised learning.
no code implementations • 19 Aug 2021 • Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
no code implementations • 20 Nov 2020 • Jing Guo, Shuping Wang, Chen Luo, Qiyu Jin, Michael Kwok-Po Ng
Local patch matching is to find similar patches in a large neighborhood which can alleviate noise effect, but the number of patches may be insufficient.
no code implementations • 24 Sep 2020 • Sayali Kulkarni, Tomer Gadot, Chen Luo, Tanya Birch, Eric Fegraus
Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field.
1 code implementation • 15 May 2020 • Lei Cai, Zhengzhang Chen, Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li, Haifeng Chen
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
1 code implementation • 14 Feb 2019 • Binhang Yuan, Chen Wang, Chen Luo, Fei Jiang, Mingsheng Long, Philip S. Yu, Yu-An Liu
Quick detection of blade ice accretion is crucial for the maintenance of wind farms.
no code implementations • 21 Feb 2018 • Chen Luo, Anshumali Shrivastava
It is well known that state-of-the-art methods for split-merge MCMC do not scale well.
no code implementations • 25 Aug 2017 • Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li
Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target?
no code implementations • 20 Jun 2017 • Chen Luo, Anshumali Shrivastava
In the big-data world existing methods fail to address the new set of memory and latency constraints.
no code implementations • 12 Nov 2016 • Dapeng Luo, Zhipeng Zeng, Nong Sang, Xiang Wu, Longsheng Wei, Quanzheng Mou, Jun Cheng, Chen Luo
In this paper, the proposed framework takes a remarkably different direction to resolve the multi-scene detection problem in a bottom-up fashion.
1 code implementation • 24 Oct 2016 • Chen Luo, Anshumali Shrivastava
However, branch and bound based pruning are only useful for very short queries (low dimensional time series), and the bounds are quite weak for longer queries.