Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through ...
Abstract— Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environmen...
Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and diffic...
We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approac...