Search Results for author:
Found 8 papers, 1 papers with code
Fusion Encoder Networks
In this paper we present fusion encoder networks (FENs): a class of algorithms for creating neural networks that map sequences to outputs.
A Hierarchical Nearest Neighbour Approach to Contextual Bandits
In this paper we consider the adversarial contextual bandit problem in metric spaces.
Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
Communication networks able to withstand hostile environments are critically important for disaster relief operations.
Reward Shaping for Happier Autonomous Cyber Security Agents
We first show that deep reinforcement learning algorithms are sensitive to the magnitude of the penalties and their relative size.
Nearest Neighbour with Bandit Feedback
In this paper we adapt the nearest neighbour rule to the contextual bandit problem.
Inroads into Autonomous Network Defence using Explained Reinforcement Learning
Computer network defence is a complicated task that has necessitated a high degree of human involvement.
Beyond COVID-19: Network science and sustainable exit strategies
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society
Differentially Private Health Tokens for Estimating COVID-19 Risk
Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0. 03 on average and thus the aggregated results can be useful in a number of identity-free contexts.
Cryptography and Security Computers and Society
