no code implementations • 20 Dec 2022 • Kelly Marchisio, Patrick Lewis, Yihong Chen, Mikel Artetxe
Prior work has shown that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen.
1 code implementation • 13 Sep 2022 • Dong Wang, Zhao Zhang, Ziwei Zhao, Yuhang Liu, Yihong Chen, LiWei Wang
Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task.
no code implementations • 13 Sep 2022 • Ziwei Zhao, Dong Wang, Yihong Chen, Ziteng Wang, LiWei Wang
In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important.
1 code implementation • 21 Jul 2022 • Hao Yang, Chen Shi, Yihong Chen, LiWei Wang
Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference.
Ranked #2 on 3D Object Detection on SUN-RGBD val
no code implementations • 20 Jul 2022 • Yihong Chen, Pushkar Mishra, Luca Franceschi, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs).
1 code implementation • AKBC 2021 • Yihong Chen, Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).
Ranked #1 on Link Prediction on CoDEx Small
1 code implementation • ICLR 2021 • Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, Yong Li
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
1 code implementation • NeurIPS 2020 • Yihong Chen, Zheng Zhang, Yue Cao, Li-Wei Wang, Stephen Lin, Han Hu
Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.
Ranked #69 on Object Detection on COCO test-dev
1 code implementation • ACL 2020 • Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on Dialogue Generation on Persona-Chat
2 code implementations • CVPR 2020 • Yihong Chen, Yue Cao, Han Hu, Li-Wei Wang
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
Ranked #6 on Video Object Detection on ImageNet VID
1 code implementation • 28 May 2019 • Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
2 code implementations • 24 May 2019 • Lin Zhu, Jiaxing Lu, Yihong Chen
By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems.
no code implementations • 13 Feb 2019 • Lin Zhu, Yihong Chen
The focus of WSDM cup 2019 is session-based sequential skip prediction, i. e. predicting whether users will skip tracks, given their immediately preceding interactions in their listening session.
no code implementations • 12 Feb 2019 • Lin Zhu, Yihong Chen, Bowen He
As one of the most popular techniques for solving the ranking problem in information retrieval, Learning-to-rank (LETOR) has received a lot of attention both in academia and industry due to its importance in a wide variety of data mining applications.
no code implementations • 22 Jun 2018 • Yihong Chen, Bei Chen, Xuguang Duan, Jian-Guang Lou, Yue Wang, Wenwu Zhu, Yong Cao
Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors.