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Found 6 papers, 1 papers with code
Boosting Fairness and Robustness in Over-the-Air Federated Learning
Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency.
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization?
Federated Learning in Wireless Networks via Over-the-Air Computations
This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large datasets and (ii) guaranteeing privacy of local agents' data.
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems?
Learning Not to Learn: Nature versus Nurture in Silico
Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth.
Code-specific policy gradient rules for spiking neurons
Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i. e., depending on which neural code is in effect.
