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Found 5 papers, 3 papers with code
Distributed Deep Learning for Persistent Monitoring of agricultural Fields
This continuous increase in the amount of data collected has created both the opportunity for, as well as the need to deploy distributed deep learning algorithms for a wide variety of decision support tasks in agriculture.
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i. e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP).
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods for control of these systems.
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
While robust control has a long history of development, robust ML is an emerging research area that has already demonstrated its relevance and urgency.
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
Under the assumptions that the cost function is convex and uncertainties enter concavely in the robust learning problem, we analytically show that our algorithm converges asymptotically to the robust optimal solution under a general adversarial budget constraints as induced by $\ell_p$ norm, for $1\leq p\leq \infty$.
