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Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored.
Approximate Pricing of Derivatives Under Fractional Stochastic Volatility Model
We investigate the problem of pricing derivatives under a fractional stochastic volatility model.
Jointly Learning to Recommend and Advertise
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
