A tree can represent “1-to-n” relations (e. g., an aspect term may correspond to multiple opinion terms) and the paths of a tree are independent and do not have orders.
Ranked #2 on Aspect-Based Sentiment Analysis (ABSA) on ASTE
To overcome these limitations, we propose a novel DRO approach that employs the Wasserstein distance instead.
In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizing the expected return itself, which is the objective in the usual approach to risk-neutral MAB.
Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy.
To address this challenge, we propose a new online risk-averse learning algorithm that relies on one-point zeroth-order estimation of the CVaR gradients computed using CVaR values that are estimated by appropriately sampling the cost functions.
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance.
Ranked #8 on Emotion Recognition in Conversation on DailyDialog
Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference").
We assume that this context is not accessible to a learner agent who can only observe the expert data.
To bypass the detection of NIDS, the generated network traffic and benign traffic are classified by a black-box NIDS.
Some recent work focused on solving a combination of two subtasks, e. g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely.
no code implementations • 28 Sep 2020 • Yufan Xu, Yi Shen, Thomas C. T. Michaels, Kevin N. Baumann, Daniele Vigolo, Quentin Peter, Yuqian Lu, Kadi L. Saar, Dominic Vella, Hongjia Zhu, Alexander P. M. Guttenplan, Marc Rodriguez-Garcia, Tuomas P. J. Knowles
Microfluidic mechanical testing revealed that the mechanical robustness of thicker-shell capsules could be controlled through modulation of the shell thickness.
Soft Condensed Matter Biological Physics
no code implementations • 25 Sep 2020 • Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, WangMeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, HaoNing Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
4 code implementations • 19 Aug 2020 • Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, Cong-Cong Li, Dragomir Anguelov
Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches.
Audio and Speech Processing Sound
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).
In this paper, we establish the grouping effect property for frame-based convex minimization models using the balanced approach.