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Found 5 papers, 1 papers with code
Distributed Online Non-convex Optimization with Composite Regret
To address these two issues, we propose a novel composite regret with a new network regret-based metric to evaluate distributed online optimization algorithms.
Asynchronous Training Schemes in Distributed Learning with Time Delay
In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance.
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+3
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings
This study proposes a deep supervised domain adaptation (DSDA) method for thermal dynamics modeling of building indoor temperature evolution and energy consumption.
On Higher-order Moments in Adam
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments.
