1 code implementation • ACL 2022 • Xin Su, Yiyun Zhao, Steven Bethard
Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation.
no code implementations • ICML 2020 • Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen
Beyond machine learning's success in the specific tasks, research for learning multiple tasks simultaneously is referred to as multi-task learning.
no code implementations • 13 May 2024 • Xin Su, Ruisi He, Peng Zhang, Bo Ai
After that, with knowledge of the optimal TDs of APs, we decouple the optimization problem into three subproblems of optimizing the baseband beamformers, RISs and TDs of RISs, respectively.
no code implementations • 7 Apr 2024 • ChenGuang Liu, Chisheng Wang, Feifei Dong, Xin Su, Chuanhua Zhu, Dejin Zhang, Qingquan Li
In this work, we study the performance that can be achieved by state-of-the-art deep learning based edge detectors in publicly available datasets when they are trained from scratch, and devise a new network architecture, the multi-stream and multi scale fusion net (msmsfnet), for edge detection.
no code implementations • 31 Jan 2024 • Weixing Liu, Jun Liu, Xin Su, Han Nie, Bin Luo
To address this challenge, we propose a practical source-free object detection (SFOD) setting for RS images, which aims to perform target domain adaptation using only the source pre-trained model.
1 code implementation • 17 Jan 2024 • HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang, Deren Li
Recently, the flourishing large language models(LLM), especially ChatGPT, have shown exceptional performance in language understanding, reasoning, and interaction, attracting users and researchers from multiple fields and domains.
no code implementations • 14 Nov 2023 • Xin Su, Tiep Le, Steven Bethard, Phillip Howard
An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge.
no code implementations • 30 Oct 2023 • Xin Su, Phillip Howard, Nagib Hakim, Steven Bethard
Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents.
no code implementations • 17 Oct 2023 • Xin Su, Yao Zhou, Zifei Shan, Qian Chen
Then we learn a semantic representation of MeKB for the cross-domain recommendation.
no code implementations • 28 Aug 2023 • HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang
These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages.
no code implementations • 23 Jul 2023 • HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang
Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks.
1 code implementation • 23 Jul 2023 • HaoNan Guo, Bo Du, Chen Wu, Xin Su, Liangpei Zhang
The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness.
no code implementations • 25 Apr 2023 • Kuo Yang, Zecong Yu, Xin Su, Xiong He, Ning Wang, Qiguang Zheng, Feidie Yu, Zhuang Liu, Tiancai Wen, Xuezhong Zhou
We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark.
no code implementations • 27 Aug 2021 • Tianren Zhang, Yizhou Jiang, Xin Su, Shangqi Guo, Feng Chen
In this paper, we present a novel supervised learning framework of learning from open-ended data, which is modeled as data implicitly sampled from multiple domains with the data in each domain obeying a domain-specific target function.
1 code implementation • SEMEVAL 2021 • Egoitz Laparra, Xin Su, Yiyun Zhao, {\"O}zlem Uzuner, Timothy Miller, Steven Bethard
Participants are then tested on data representing a new (target) domain.
no code implementations • SEMEVAL 2021 • Xin Su, Yiyun Zhao, Steven Bethard
This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing.
no code implementations • 14 May 2021 • Xin Su, Timothy Miller, Xiyu Ding, Majid Afshar, Dmitriy Dligach
Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria.
no code implementations • 28 Jan 2021 • Qi Gu, Dan Wu, Xin Su, Jing Jin, Yifei Yuan, Jiangzhou Wang
On the other hand, a relay node in a traditional relay network has to be active, which indicates that it will consume energy when it is relaying the signal or information between the source and destination nodes.
Information Theory Information Theory
no code implementations • 18 Dec 2020 • Chengyuan Li, Jun Liu, Hailong Hong, Wenju Mao, Chenjie Wang, Chudi Hu, Xin Su, Bin Luo
On the basis of this, a novel octave convolution-based semantic attention feature pyramid network (OcSaFPN) is proposed to get higher accuracy in object detection with noise.
no code implementations • 10 Mar 2020 • Chenjie Wang, Bin Luo, Yun Zhang, Qing Zhao, Lu Yin, Wei Wang, Xin Su, Yajun Wang, Chengyuan Li
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
no code implementations • MDPI Remote Sensing 2020 • Jianhao Gao, Qiangqiang Yuan, Jie Li, Hai Zhang, Xin Su
The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function.
Ranked #6 on Cloud Removal on SEN12MS-CR
no code implementations • 25 Sep 2019 • Zhile Yang*, Haichuan Gao*, Xin Su, Shangqi Guo, Feng Chen
In this paper, Subjective Reinforcement Learning Framework is proposed to state the problem from a broader and systematic view, and subjective policy is proposed to represent existing related algorithms in general.
no code implementations • 9 Sep 2019 • Xin Su, Shangqi Guo, Feng Chen
The construction of artificial general intelligence (AGI) was a long-term goal of AI research aiming to deal with the complex data in the real world and make reasonable judgments in various cases like a human.
no code implementations • 8 Mar 2019 • Mira Rizkallah, Xin Su, Thomas Maugey, Christine Guillemot
Both non separable and separable transforms are considered.