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Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons
When training deep neural networks, the phenomenon of $\textit{dying neurons}$ $\unicode{x2013}$units that become inactive or saturated, output zero during training$\unicode{x2013}$ has traditionally been viewed as undesirable, linked with optimization challenges, and contributing to plasticity loss in continual learning scenarios.
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments.
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