1.6k citations · 2k across the 18 of their papers we have counts for
18 papers
Ten Years of Deep Learning for Wireless Communications: From Learned Blocks to Deployable Wireless Intelligence
Hao Ye, Geoffrey Ye Li, Biing-Hwang Juang
Over the past decade, deep learning has evolved from a tool for replacing isolated wireless blocks into a broader methodology for developing wireless intelligence. This article tra…
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang
Tracking dialogue states is an essential topic in task-oriented dialogue systems, which involve filling in the necessary information in pre-defined slots corresponding to a schema.…
Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking
Ruolin Su, Jingfeng Yang, Ting-Wei Wu +1
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dia…
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang
As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing…
Knowledge Augmented BERT Mutual Network in Multi-turn Spoken Dialogues
Ting-Wei Wu, Biing-Hwang Juang
Modern spoken language understanding (SLU) systems rely on sophisticated semantic notions revealed in single utterances to detect intents and slots. However, they lack the capabili…
Accretionary Learning with Deep Neural Networks
Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang +2
One of the fundamental limitations of Deep Neural Networks (DNN) is its inability to acquire and accumulate new cognitive capabilities. When some new data appears, such as new obje…
A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection
Ting-Wei Wu, Ruolin Su, Biing-Hwang Juang
The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog a…
Deep Learning Enabled Semantic Communication Systems
Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li +1
Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make jo…
Active Voice Authentication
Zhong Meng, M Umair Bin Altaf, Biing-Hwang +1
Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an au…
Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
Hao Ye, Le Liang, Geoffrey Ye Li +1
In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), in which DNNs are employed to perform several key functions, including en…
Cycle-Consistent Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature mapping using deep neural networks is an effective approach for single-channel speech enhancement. Noisy features are transformed to the enhanced ones through a mapping net…
Adversarial Feature-Mapping for Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature-mapping with deep neural networks is commonly used for single-channel speech enhancement, in which a feature-mapping network directly transforms the noisy features to the c…
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such a…
Deep Learning in Physical Layer Communications
Zhijin Qin, Hao Ye, Geoffrey Ye Li +1
Deep learning (DL) has shown the great potentials to break the bottleneck of communication systems. This article provides an overview on the recent advancements in DL-based physica…
Deep Reinforcement Learning based Resource Allocation for V2V Communications
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang
In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to bo…
Speaker-Invariant Training via Adversarial Learning
Zhong Meng, Jinyu Li, Zhuo Chen +5
We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to…
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
Xiangwei Zhou, Mingxuan Sun, Geoffrey Ye Li +1
The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent w…
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang
This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely ad…