8 citations · 9 across the 4 of their papers we have counts for
4 papers
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)
Nihal Acharya Adde, Alexandra Gianzina, Hanno Gottschalk +1
This paper introduces Evolutionary Multi-Objective Network Architecture Search (EMNAS) for the first time to optimize neural network architectures in large-scale Reinforcement Lear…
Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
Nihal Acharya Adde, Hanno Gottschalk, Andreas Ebert
This paper focuses on hyperparameter optimization for autonomous driving strategies based on Reinforcement Learning. We provide a detailed description of training the RL agent in a…
Finding hidden-feature depending laws inside a data set and classifying it using Neural Network
Thilo Moshagen, Nihal Acharya Adde, Ajay Navilarekal Rajgopal
The logcosh loss function for neural networks has been developed to combine the advantage of the absolute error loss function of not overweighting outliers with the advantage of th…
Classification based on invisible features and thereby finding the effect of tuberculosis vaccine on COVID-19
Nihal Acharya Adde, Thilo Moshagen
In the case of clustered data, an artificial neural network with logcosh loss function learns the bigger cluster rather than the mean of the two. Even more so, the ANN when used fo…