7 citations · 21 across the 8 of their papers we have counts for
8 papers
Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features
Cunhang Fan, Bin Liu, Jianhua Tao +2
Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speaker-independent speech separation. DC is usually formulated…
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Bin Liu, Grigorios Tsoumakas
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with th…
Collective Dynamics and Control for Multiple Unmanned Surface Vessels
Bin Liu, Zhiyong Chen, Hai-Tao Zhang +4
A multi-unmanned surface vessel (USV) formation control system is established on a novel platform composed of three 1.2 meter-long hydraulic jet propulsion surface vessels, a diffe…
Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization
Bin Liu
Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhe…
A Survey on Trust Modeling from a Bayesian Perspective
Bin Liu
In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature,…
Online Fault-Tolerant Dynamic Event Region Detection in Sensor Networks via Trust Model
Jiejie Wang, Bin Liu
This paper proposes a Bayesian modeling approach to address the problem of online fault-tolerant dynamic event region detection in wireless sensor networks. In our model every netw…
Robust Particle Filter by Dynamic Averaging of Multiple Noise Models
Bin Liu
State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing t…
Posterior Exploration based Sequential Monte Carlo for Global Optimization
Bin Liu
We propose a global optimization algorithm based on the Sequential Monte Carlo (SMC) sampling framework. In this framework, the objective function is normalized to be a probabilist…