Stochastic and Adversarial Online Learning without Hyperparameters

no code implementations • NeurIPS 2017 • Ashok Cutkosky, Kwabena A. Boahen

Most online optimization algorithms focus on one of two things: performing well in adversarial settings by adapting to unknown data parameters (such as Lipschitz constants), typically achieving $O(\sqrt{T})$ regret, or performing well in stochastic settings where they can leverage some structure in the losses (such as strong convexity), typically achieving $O(\log(T))$ regret.

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm

no code implementations • NeurIPS 2011 • Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen

The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF).

Decoder Translation