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Hyperparameter Tuning for Deep Reinforcement Learning Applications
Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a wide variety of applications from controlling simple pendulums to complex data centers.
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering
Traffic optimization challenges, such as load balancing, flow scheduling, and improving packet delivery time, are difficult online decision-making problems in wide area networks (WAN).
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization
We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models.
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers.
Do optimization methods in deep learning applications matter?
With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention.
