no code implementations • SemEval (NAACL) 2022 • Ye Wang, Yanmeng Wang, Baishun Ling, Zexiang Liao, Shaojun Wang, Jing Xiao
This paper describes the second-placed system for subtask 2 and the ninth-placed system for subtask 1 in SemEval 2022 Task 4: Patronizing and Condescending Language Detection.
1 code implementation • 28 Dec 2022 • Ye Wang, Rui Ma, Xiaoqing Ma, Honghua Cui, Yubin Xiao, Xuan Wu, You Zhou
BMEC contains 5, 666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells.
no code implementations • 7 Dec 2022 • Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu, Jian Lin, Tianpeng Wu, Ye Wang, Yu Fu, Lin Feng, Kangkang Gao, Zeyu Liu, Yuanzhe Pang, Chengqi Duan, Huipeng Zhou, Yajie Wang, Yuhang Zhao, Shangbo Wu, Haoran Lyu, Zhiyu Lin, YiFei Gao, Shuang Li, Haonan Wang, Jitao Sang, Chen Ma, Junhao Zheng, Yijia Li, Chao Shen, Chenhao Lin, Zhichao Cui, Guoshuai Liu, Huafeng Shi, Kun Hu, Mengxin Zhang
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
1 code implementation • 17 Oct 2022 • Ye Wang, Xinxin Liu, Wenxin Hu, Tao Zhang
To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning framework - shift and squared ranking loss positive-unlabeled (SSR-PU) learning.
no code implementations • 17 Oct 2022 • Wenlu Wang, Ye Wang, Honggang Zhao, Simone Sciabola
In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 10^60 - 10^100.
no code implementations • 6 Oct 2022 • Meng Yuan, Ye Wang, Lei LI, Tianyou Chai, Wei Tech Ang
Electric-powered wheelchair plays an important role in providing accessibility for people with mobility impairment.
no code implementations • 29 Sep 2022 • Toshiaki Koike-Akino, Ye Wang
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications.
no code implementations • 20 Sep 2022 • Haifeng Xia, Pu, Wang, Toshiaki Koike-Akino, Ye Wang, Philip Orlik, Zhengming Ding
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning.
no code implementations • 26 Aug 2022 • Ye Wang, Qi Zhao, Wenyan Wu, Ailsa Willis, Angus R. Simpson, Erik Weyer
This paper presents a case study of the operational management of the Robinvale high-pressure piped irrigation water delivery system (RVHPS) in Australia.
no code implementations • 26 Aug 2022 • Yujia Yang, Ye Wang, Chris Manzie, Ye Pu
In response, we propose a novel real-time DMPC framework with a quantization refinement scheme that updates the quantization parameters on-line so that both the quantization noise and the optimization sub-optimality decrease asymptotically.
1 code implementation • 25 Aug 2022 • Ye Wang, Yujia Yang, Ye Pu, Chris Manzie
We seek to combine the nonlinear modeling capabilities of a wide class of neural networks with the safety guarantees of model predictive control (MPC) in a rigorous and online computationally tractable framework.
no code implementations • 26 Jul 2022 • Ye Wang, Jingbo Liao, Hong Yu, Guoyin Wang, Xiaoxia Zhang, Li Liu
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
1 code implementation • 20 Jul 2022 • Longshen Ou, Xiangming Gu, Ye Wang
To fill in the performance gap between ALT and ASR, we attempt to exploit the similarities between speech and singing.
1 code implementation • 13 Jul 2022 • Xiangming Gu, Longshen Ou, Danielle Ong, Ye Wang
Automatic lyric transcription (ALT) is a nascent field of study attracting increasing interest from both the speech and music information retrieval communities, given its significant application potential.
no code implementations • Findings (EMNLP) 2021 • Yanmeng Wang, Jun Bai, Ye Wang, Jianfei Zhang, Wenge Rong, Zongcheng Ji, Shaojun Wang, Jing Xiao
To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage.
no code implementations • 17 May 2022 • Toshiaki Koike-Akino, Pu Wang, Ye Wang
Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment.
no code implementations • 17 May 2022 • Toshiaki Koike-Akino, Pu Wang, Ye Wang
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment.
no code implementations • 17 May 2022 • Bryan Liu, Toshiaki Koike-Akino, Ye Wang, Kieran Parsons
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.
no code implementations • 17 May 2022 • Bryan Liu, Toshiaki Koike-Akino, Ye Wang, Kieran Parsons
This paper investigates a turbo receiver employing a variational quantum circuit (VQC).
2 code implementations • 27 Apr 2022 • Jiahong Zhang, Meijun Qu, Ye Wang, Lihong Cao
Unlike previous attention mechanisms that handle pixel-level, channel-level, or patch-level features, MPA focuses on features at the image level.
no code implementations • 8 Apr 2022 • Longshen Ou, Ziyi Guo, Emmanouil Benetos, Jiqing Han, Ye Wang
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation.
no code implementations • ACL 2022 • Kai Chen, Ye Wang, Yitong Li, Aiping Li
Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
no code implementations • 11 Feb 2022 • Agostino Capponi, Ruizhe Jia, Ye Wang
A 1% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0. 6%.
no code implementations • 28 Dec 2021 • Jianyuan Yu, Pu, Wang, Toshiaki Koike-Akino, Ye Wang, Philip V. Orlik, R. Michael Buehrer
The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights.
no code implementations • 17 Dec 2021 • Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label.
no code implementations • 1 Nov 2021 • Safa C. Medin, Bernhard Egger, Anoop Cherian, Ye Wang, Joshua B. Tenenbaum, Xiaoming Liu, Tim K. Marks
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis.
no code implementations • 19 Oct 2021 • Bo Pang, Yongquan Fu, Siyuan Ren, Ye Wang, Qing Liao, Yan Jia
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
1 code implementation • CVPR 2022 • Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
no code implementations • 29 Sep 2021 • Xueyang Wu, Hengguan Huang, Hao Wang, Ye Wang, Qian Xu
However, it is challenging for GANs to model distributions of separate non-i. i. d.
no code implementations • 28 Sep 2021 • Qianmengke Zhao, Ye Wang, Qun Liu
Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand.
no code implementations • SEMEVAL 2021 • Ye Wang, Yanmeng Wang, Haijun Zhu, Bo Zeng, Zhenghong Hao, Shaojun Wang, Jing Xiao
This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning.
1 code implementation • 17 Jul 2021 • Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, Ye Wang
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
no code implementations • 16 Jun 2021 • Andac Demir, Toshiaki Koike-Akino, Ye Wang, Masaki Haruna, Deniz Erdogmus
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks.
no code implementations • 28 May 2021 • Lioba Heimbach, Ye Wang, Roger Wattenhofer
In this paper, we aim to understand how liquidity providers react to market information and how they benefit from providing liquidity in DEXes.
no code implementations • 10 May 2021 • Shuqi Dai, Xichu Ma, Ye Wang, Roger B. Dannenberg
Many practices have been presented in music generation recently.
no code implementations • 21 Apr 2021 • Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou, Shuiguang Deng, Roger Wattenhofer
We find that traders have executed 292, 606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue.
no code implementations • 13 Apr 2021 • Kaitai Zhang, Bin Wang, Hong-Shuo Chen, Ye Wang, Shiyu Mou, C. -C. Jay Kuo
The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos.
no code implementations • 28 Feb 2021 • Jialin Peng, Ye Wang
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks.
1 code implementation • 23 Nov 2020 • Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang, Ye Wang, Pradeep Kumar Jayaraman, Krishna Murthy Jatavallabhula
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
no code implementations • 15 Oct 2020 • Ye Wang, Kevin Too Yok, Wenyan Wu, Angus R. Simpson, Erik Weyer, Chris Manzie
In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed.
no code implementations • 28 Sep 2020 • Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
no code implementations • 26 Aug 2020 • Mo Han, Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
no code implementations • 17 Aug 2020 • Qiang Liu, Tao Han, Ning Zhang, Ye Wang
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond.
no code implementations • 22 Jul 2020 • Ye Wang, Shuchin Aeron, Adnan Siraj Rakin, Toshiaki Koike-Akino, Pierre Moulin
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.
no code implementations • 17 Jul 2020 • Wenjie Chen, Fengtong Du, Ye Wang, Lihong Cao
Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain.
no code implementations • ICML 2020 • Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
no code implementations • 2 Jul 2020 • Andac Demir, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
no code implementations • 6 May 2020 • Toshiaki Koike-Akino, Ye Wang
This is motivated by the rateless property of conventional PCA, where the least important principal components can be discarded to realize variable rate dimensionality reduction that gracefully degrades the distortion.
no code implementations • 15 Apr 2020 • Mo Han, Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
1 code implementation • CVPR 2020 • Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities.
Ranked #1 on
Face Alignment
on Menpo
no code implementations • 15 Feb 2020 • Sairamvinay Vijayaraghavan, Ye Wang, Zhiyuan Guo, John Voong, Wenda Xu, Armand Nasseri, Jiaru Cai, Linda Li, Kevin Vuong, Eshan Wadhwa
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
no code implementations • 22 Nov 2019 • Toshiaki Koike-Akino, Ye Wang, David S. Millar, Keisuke Kojima, Kieran Parsons
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts.
no code implementations • 27 Mar 2019 • Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
no code implementations • 9 Mar 2019 • Ye Wang, Toshiaki Koike-Akino
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs.
no code implementations • 9 Mar 2019 • Toshiki Matsumine, Toshiaki Koike-Akino, Ye Wang
This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node.
no code implementations • 19 Dec 2018 • Ye Wang, Yueru Chen, Jongmoo Choi, C. -C. Jay Kuo
One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location.
no code implementations • 19 Dec 2018 • Ye Wang, Jongmoo Choi, Yueru Chen, Siyang Li, Qin Huang, Kaitai Zhang, Ming-Sui Lee, C. -C. Jay Kuo
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
no code implementations • 17 Dec 2018 • Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).
no code implementations • 13 Dec 2018 • Ye Wang, Jongmoo Choi, Yueru Chen, Qin Huang, Siyang Li, Ming-Sui Lee, C. -C. Jay Kuo
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
no code implementations • 11 Jun 2018 • Ye Wang, Mi Lu
Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways.
no code implementations • 21 May 2018 • Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent.
no code implementations • 11 Apr 2018 • Han Wang, Ye Wang, Xinxiang Zhang, Mi Lu, Yoonsuck Choe, Jingjing Cao
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge.
no code implementations • 19 Dec 2017 • Ardhendu Tripathy, Ye Wang, Prakash Ishwar
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
no code implementations • 20 Jul 2017 • Qin Huang, Chunyang Xia, Chi-Hao Wu, Siyang Li, Ye Wang, Yuhang Song, C. -C. Jay Kuo
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation.
Ranked #55 on
Semantic Segmentation
on NYU Depth v2
no code implementations • NeurIPS 2015 • Ye Wang, David B. Dunson
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning.
no code implementations • 6 Nov 2013 • Xinxi Wang, Yi Wang, David Hsu, Ye Wang
Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings.