10 citations · 14 across the 5 of their papers we have counts for
5 papers
Sparse neural networks with skip-connections for identification of aluminum electrolysis cell
Erlend Torje Berg Lundby, Haakon Robinsson, Adil Rasheed +2
Neural networks are rapidly gaining interest in nonlinear system identification due to the model's ability to capture complex input-output relations directly from data. However, de…
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson, Erlend Lundby, Adil Rasheed +1
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However,…
Physics guided neural networks for modelling of non-linear dynamics
Haakon Robinson, Suraj Pawar, Adil Rasheed +1
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns fro…
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Eivind Meyer, Haakon Robinson, Adil Rasheed +1
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the…
Dissecting Deep Neural Networks
Haakon Robinson, Adil Rasheed, Omer San
In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. Howev…