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Found 6 papers, 0 papers with code
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound.
Entropy Regularized Reinforcement Learning with Cascading Networks
Compared to these learning frameworks, one of the major difficulties of RL is the absence of i. i. d.
Survey on Large Scale Neural Network Training
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training.
Efficient Combination of Rematerialization and Offloading for Training DNNs
Rematerialization and offloading are two well known strategies to save memory during the training phase of deep neural networks, allowing data scientists to consider larger models, batch sizes or higher resolution data.
Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm.
Training on the Edge: The why and the how
Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distribute the computing power and try to do as much processing as possible, close to the source of the data.
Distributed, Parallel, and Cluster Computing
