Search Results for author: Ye Wang

Found 39 papers, 3 papers with code

CGNN: Traffic Classification with Graph Neural Network

no code implementations19 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.

Classification Traffic Classification

CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation

no code implementations6 Oct 2021 Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero

While recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale.

Text-to-Image Generation

Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models

no code implementations28 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.

Decision Making Image Classification

PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning

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.

Denoising Language Modelling +1

STRODE: Stochastic Boundary Ordinary Differential Equation

1 code implementation17 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.

automatic-speech-recognition Point Processes +2

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals

no code implementations16 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.


Behavior of Liquidity Providers in Decentralized Exchanges

no code implementations28 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.

Cyclic Arbitrage in Decentralized Exchange Markets

no code implementations21 Apr 2021 Ye Wang, Yan Chen, Shuiguang Deng, Roger Wattenhofer

Almost surely, the three floating exchange rates are not perfectly in sync, which opens up arbitrage possibilities for cyclic trading.

Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations

no code implementations13 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.

Texture Synthesis

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

no code implementations28 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.

Medical Image Segmentation

Minimizing Pumping Energy Cost in Real-time Operations of Water Distribution Systems using Economic Model Predictive Control

no code implementations15 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.

Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

no code implementations28 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.

Representation Learning

Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

no code implementations26 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.

Transfer Learning

DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing

no code implementations17 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.

Robust Machine Learning via Privacy/Rate-Distortion Theory

no code implementations22 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.

A Biologically Plausible Audio-Visual Integration Model for Continual Learning

no code implementations17 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.

Continual Learning

Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

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.

automatic-speech-recognition Speech Recognition

AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference

no code implementations2 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.

Bayesian Inference Ensemble Learning +2

Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction

no code implementations6 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.

Dimensionality Reduction

Disentangled Adversarial Transfer Learning for Physiological Biosignals

no code implementations15 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.

Transfer Learning

LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood

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.

Face Alignment

Fake News Detection with Different Models

no code implementations15 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.

Fake News Detection

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

no code implementations22 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.

Adversarial Deep Learning in EEG Biometrics

no code implementations27 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.

EEG Person Identification +1

Learning to Modulate for Non-coherent MIMO

no code implementations9 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.

Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks

no code implementations9 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.

Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation

no code implementations19 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.

Instance Segmentation Semantic Segmentation +2

Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks

no code implementations19 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.

Data Augmentation

Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

no code implementations17 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).

EEG Representation Learning +1

Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation

no code implementations13 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.

Instance Segmentation Object Tracking +3

An optimized system to solve text-based CAPTCHA

no code implementations11 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.

Invariant Representations from Adversarially Censored Autoencoders

no code implementations21 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.

Style Transfer

English Out-of-Vocabulary Lexical Evaluation Task

no code implementations11 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.

Classification General Classification

Privacy-Preserving Adversarial Networks

no code implementations19 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.

Semantic Segmentation with Reverse Attention

no code implementations20 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.

Semantic Segmentation

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process

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.

Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach

no code implementations6 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.

Bayesian Inference Recommendation Systems +1

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