5 papers
Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks
Todd Morrill, Christian Pehle, Anthony Zador
Continuous-time, event-native spiking neural networks (SNNs) operate strictly on spike events, treating spike timing and ordering as the representation rather than an artifact of t…
Riemannian Optimization in Modular Systems
Christian Pehle, Jean-Jacques Slotine
Understanding how systems built out of modular components can be jointly optimized is an important problem in biology, engineering, and machine learning. The backpropagation algori…
Unlocked Backpropagation using Wave Scattering
Christian Pehle, Jean-Jacques Slotine
Both the backpropagation algorithm in machine learning and the maximum principle in optimal control theory are posed as a two-point boundary problem, resulting in a "forward-backwa…
Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation
Ari S. Benjamin, Kyle Daruwalla, Christian Pehle +2
One frequently wishes to learn a range of similar tasks as efficiently as possible, re-using knowledge across tasks. In artificial neural networks, this is typically accomplished b…
Continual learning with the neural tangent ensemble
Ari S. Benjamin, Christian Pehle, Kyle Daruwalla
A natural strategy for continual learning is to weigh a Bayesian ensemble of fixed functions. This suggests that if a (single) neural network could be interpreted as an ensemble, o…