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20232026
most citedLearning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs

2 citations · 2 across the 9 of their papers we have counts for

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eess.SY2026

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning

Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1

This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce…

eess.SY2026

Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning

Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1

This paper studies contractivity of firing-rate and Hopfield recurrent neural networks. We derive sharp LMI conditions on the synaptic matrices that characterize contractivity of b…