Search Results for author: Xin Su

Found 10 papers, 1 papers with code

Task Understanding from Confusing Multi-task Data

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.

Multi-Task Learning

Subjective Learning for Open-Ended Data

no code implementations27 Aug 2021 Tianren Zhang, Yizhou Jiang, Xin Su, Shangqi Guo, Feng Chen

Conventional machine learning methods typically assume that data is split according to tasks, and the data in each task can be modeled by a single target function.

The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation

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.

Active Learning Data Augmentation +2

Classifying Long Clinical Documents with Pre-trained Transformers

no code implementations14 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.


Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator

no code implementations28 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

Object Detection based on OcSaFPN in Aerial Images with Noise

no code implementations18 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.

Denoising Object Detection

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

no code implementations10 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.

Motion Segmentation

Subjectivity Learning Theory towards Artificial General Intelligence

no code implementations9 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.

Learning Theory

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