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Found 2 papers, 1 papers with code
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
Increasing the mini-batch size for stochastic gradient descent offers significant opportunities to reduce wall-clock training time, but there are a variety of theoretical and systems challenges that impede the widespread success of this technique.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information.
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