**Zoneout** is a  method for regularizing [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks). At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like [dropout](https://paperswithcode.com/method/dropout), zoneout uses random noise to train a pseudo-ensemble, improving generalization.
But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward [stochastic depth](https://paperswithcode.com/method/stochastic-depth) networks.

**PyTorch DDP** (Distributed Data Parallel) is a distributed data parallel implementation for PyTorch. To guarantee mathematical equivalence, all replicas start from the same initial values for model parameters and synchronize gradients to keep parameters consistent across training iterations. To minimize the intrusiveness, the implementation exposes the same forward API as the user model, allowing applications to seamlessly replace subsequent occurrences of a user model with the distributed data parallel model object with no additional code changes. Several techniques are integrated into the design to deliver high-performance training, including bucketing gradients, overlapping communication with computation, and skipping synchronization.

PyTorch DDP

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Zoneout

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

SAGA is a method in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem.

Source	Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com