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Found 4 papers, 0 papers with code
Towards Reproducible Machine Learning Research in Natural Language Processing
While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility.
Does DQN really learn? Exploring adversarial training schemes in Pong
In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained against the built-in Atari opponent.
Streamlining Tensor and Network Pruning in PyTorch
In order to contrast the explosion in size of state-of-the-art machine learning models that can be attributed to the empirical advantages of over-parametrization, and due to the necessity of deploying fast, sustainable, and private on-device models on resource-constrained devices, the community has focused on techniques such as pruning, quantization, and distillation as central strategies for model compression.
On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
We examine how recently documented, fundamental phenomena in deep learning models subject to pruning are affected by changes in the pruning procedure.
