FAD
发表于 2025-3-21 18:03:29
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epicondylitis
发表于 2025-3-21 23:34:59
Speeding Learning since the time BP was first introduced, BP is still the most widely used learning algorithm.The reason for this is its simplicity, efficiency, and its general effectiveness on a wide range of problems. Even so, there are many pitfalls in applying it, which is where all these tricks enter.
gain631
发表于 2025-3-22 04:18:02
Early Stopping — But When? 12 problems and 24 different network architectures I conclude slower stopping criteria allow for small improvements in generalization (here: about 4% on average), but cost much more training time (here: about factor 4 longer on average).
VEST
发表于 2025-3-22 05:06:37
A Simple Trick for Estimating the Weight Decay Parametermator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute..The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stopping.
breadth
发表于 2025-3-22 10:50:48
Centering Neural Network Gradient Factorsated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network’s generalization ability.
Campaign
发表于 2025-3-22 16:00:01
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Commemorate
发表于 2025-3-22 19:00:55
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micronutrients
发表于 2025-3-22 22:47:23
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勉励
发表于 2025-3-23 01:28:22
Efficient BackPropations of why they work..Many authors have suggested that second-order optimization methods are advantageous for neural net training. It is shown that most “classical” second-order methods are impractical for large neural networks. A few methods are proposed that do not have these limitations.
Noisome
发表于 2025-3-23 09:34:49
Large Ensemble Averaginghoices of synaptic weights. We find that the optimal stopping criterion for large ensembles occurs later in training time than for single networks. We test our method on the suspots data set and obtain excellent results.