2.6k citations · 2.8k across the 24 of their papers we have counts for
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Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains
Aqsa Saeed Qureshi, Asifullah Khan
Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also p…
Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power
Asifullah Khan, Aneela Zameer, Tauseef Jamal +1
Wind energy forecasting helps to manage power production, and hence, reduces energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the human brain and thus posses…
A New Channel Boosted Convolutional Neural Network using Transfer Learning
Asifullah Khan, Anabia Sohail, Amna Ali
We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of…