6 citations · 15 across the 5 of their papers we have counts for
6 papers
Continual Learning Using Multi-view Task Conditional Neural Networks
Honglin Li, Payam Barnaghi, Shirin Enshaeifar +1
Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known…
Continual Learning Using Bayesian Neural Networks
HongLin Li, Payam Barnaghi, Shirin Enshaeifar +1
Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using…
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
Honglin Li, Shirin Enshaeifar, Frieder Ganz +1
Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning…
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Honglin Li, Frieder Ganz, Shirin Enshaeifar +1
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the prev…
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
Alireza Ahrabian, Shirin Enshaeifar, Clive Cheong-Took +1
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the paramete…
Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing
Min Xiang, Shirin Enshaeifar, Alexander E. Stott +4
Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the stan…