Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training.
With the increasing prevalence of video recordings there is a growing need for tools that can maintain the privacy of those recorded.
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go.
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations?
Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions?
This paper investigates how working of Convolutional Neural Network (CNN) can be explained through visualization in the context of machine perception of autonomous vehicles.