60 citations · 63 across the 3 of their papers we have counts for
5 papers
Deep Hedging of Derivatives Using Reinforcement Learning
Jay Cao, Jacky Chen, John Hull +1
This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by…
Variational Autoencoders: A Hands-Off Approach to Volatility
Maxime Bergeron, Nicholas Fung, John Hull +1
A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a fu…
Training CNNs faster with Dynamic Input and Kernel Downsampling
Zissis Poulos, Ali Nouri, Andreas Moshovos
We reduce training time in convolutional networks (CNNs) with a method that, for some of the mini-batches: a) scales down the resolution of input images via downsampling, and b) re…
Astraea: A Decentralized Blockchain Oracle
John Adler, Ryan Berryhill, Andreas Veneris +3
The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous o…
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
Alberto Delmas, Patrick Judd, Dylan Malone Stuart +5
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations…