S
Saeid Nahavandi
Researcher at Deakin University
Publications - 1049
Citations - 24637
Saeid Nahavandi is an academic researcher from Deakin University. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 58, co-authored 987 publications receiving 16699 citations. Previous affiliations of Saeid Nahavandi include Delft University of Technology & Monash University.
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Dynamic nanofin heat sinks
Pyshar Yi,Khashayar Khoshmanesh,Adam F. Chrimes,Jos L. Campbell,Kamran Ghorbani,Saeid Nahavandi,Gary Rosengarten,Kourosh Kalantar-zadeh +7 more
TL;DR: In this article, a magnetophoretically formed high aspect ratio nano-nodes are used for hot-spot cooling in microfluidic environments, which can be dynamically chained and docked onto the hot spots to establish tuneable high-aspect ratio nanofins for the heat exchange between these hot spots and the liquid coolant.
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A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
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Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications
TL;DR: A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning.
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Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
TL;DR: A new, fast, yet reliable method for the construction of PIs for NN predictions, and the quantitative comparison with three traditional techniques for prediction interval construction reveals that the LUBE method is simpler, faster, and more reliable.
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Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
TL;DR: The quality of PIs produced by the combiners is dramatically better than the quality ofPIs obtained from each individual method and a new method for generating combined PIs using the traditional PIs is proposed.