11 citations · 26 across the 5 of their papers we have counts for
8 papers
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future
Aras Dargazany
Combination of machine learning (for generating machine intelligence), computer vision (for better environment perception), and robotic systems (for controlled environment interact…
Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI
Aras Dargazany
Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications t…
Deep learning research landscape & roadmap in a nutshell: past, present and future -- Towards deep cortical learning
Aras R. Dargazany
The past, present and future of deep learning is presented in this work. Given this landscape & roadmap, we predict that deep cortical learning will be the convergence of deep lear…
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing
Aras R. Dargazany, Mohammadreza Abtahi, Kunal Mankodiya
Brain computer interfaces (BCI) using EEG, fNIRS and body motion (MoCap) data are getting more attention due to the fact that fNIRS and MoCap are not prone to movement artifacts si…
Stereo-based terrain traversability analysis using normal-based segmentation and superpixel surface analysis
Aras R. Dargazany
In this paper, an stereo-based traversability analysis approach for all terrains in off-road mobile robotics, e.g. Unmanned Ground Vehicles (UGVs) is proposed. This approach reform…
Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning
Aras R. Dargazany
This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an itera…