223 citations · 522 across the 34 of their papers we have counts for
5 papers · 1 filter
Variational End-to-End Navigation and Localization
Alexander Amini, Guy Rosman, Sertac Karaman +1
Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle form…
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
Ramin M. Hasani, Mathias Lechner, Alexander Amini +2
In this paper, we introduce the notion of liquid time-constant (LTC) recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with varying neuronal time-constant reali…
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
Ramin M. Hasani, Alexander Amini, Mathias Lechner +3
In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a s…
Can a Compact Neuronal Circuit Policy be Re-purposed to Learn Simple Robotic Control?
Ramin Hasani, Mathias Lechner, Alexander Amini +2
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control ta…
Spatial Uncertainty Sampling for End-to-End Control
Alexander Amini, Ava Soleimany, Sertac Karaman +1
End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-…