Search Results for author:
Found 7 papers, 0 papers with code
MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings
Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry.
Deeper Hedging: A New Agent-based Model for Effective Deep Hedging
With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels.
MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration
This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows.
Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model
It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation.
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach
We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios.
An Analysis of Alternating Direction Method of Multipliers for Feed-forward Neural Networks
This is while the use of matrix inversion, which is challenging for hardware implementation, is avoided in this method.
An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR
In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training.
