3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Where2Start: Leveraging initial States for Robust and Sample-Efficient Reinforcement Learning
Pouya Parsa, Raoof Zare Moayedi, Mohammad Bornosi +1
The reinforcement learning algorithms that focus on how to compute the gradient and choose next actions, are effectively improved the performance of the agents. However, these algo…
Adaptive Low-Rank Regularization with Damping Sequences to Restrict Lazy Weights in Deep Networks
Mohammad Mahdi Bejani, Mehdi Ghatee
Overfitting is one of the critical problems in deep neural networks. Many regularization schemes try to prevent overfitting blindly. However, they decrease the convergence speed of…
Adaptive Low-Rank Factorization to regularize shallow and deep neural networks
Mohammad Mahdi Bejani, Mehdi Ghatee
The overfitting is one of the cursing subjects in the deep learning field. To solve this challenge, many approaches were proposed to regularize the learning models. They add some h…
Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study
Mohammad Mahdi Bejani, Mehdi Ghatee
Intelligent Transportation Systems (ITS) are much correlated with data science mechanisms. Among the different correlation branches, this paper focuses on the neural network learni…