137 citations · 510 across the 88 of their papers we have counts for
6 papers · 1 filter
Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift
Julian Lemmel, Pedro D. Wendel Garcia, Taisuke Kobayashi +1
Deploying a pretrained controller exposes it to conditions that are absent from its training data. Sensor drift, outright sensor failure and accumulating measurement noise all indu…
Synaptic Activation and Dual Liquid Dynamics for Interpretable Bio-Inspired Models
Mónika Farsang, Radu Grosu
In this paper, we present a unified framework for various bio-inspired models to better understand their structural and functional differences. We show that liquid-capacitance-exte…
Liquid Resistance Liquid Capacitance Networks
Mónika Farsang, Sophie A. Neubauer, Radu Grosu
We introduce liquid-resistance liquid-capacitance neural networks (LRCs), a neural-ODE model which considerably improve the generalization, accuracy, and biological plausibility of…
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
Mónika Farsang, Mathias Lechner, David Lung +3
Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems. Bio-electrical synapses directly…
On the Benefits of Biophysical Synapses
Julian Lemmel, Radu Grosu
The approximation capability of ANNs and their RNN instantiations, is strongly correlated with the number of parameters packed into these networks. However, the complexity barrier…
Worm-level Control through Search-based Reinforcement Learning
Mathias Lechner, Radu Grosu, Ramin M. Hasani
Through natural evolution, nervous systems of organisms formed near-optimal structures to express behavior. Here, we propose an effective way to create control agents, by \textit{r…