2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2020
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Roman Pogodin, Peter E. Latham
The state-of-the art machine learning approach to training deep neural networks, backpropagation, is implausible for real neural networks: neurons need to know their outgoing weigh…
q-bio.NC2019
Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks
Roman Pogodin, Dane Corneil, Alexander Seeholzer +2
Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or t…
cs.LG2019★ 2 cited
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Roman Pogodin, Tor Lattimore
We make three contributions to the theory of k-armed adversarial bandits. First, we prove a first-order bound for a modified variant of the INF strategy by Audibert and Bubeck [200…