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Furthermore, batch normalization seems to have a regularizing effect such that the network improves its generalization properties, and it is thus unnecessary to use dropout to mitigate overfitting. It has also been observed that the network becomes more robust to different initialization schemes and learning rates while using batch normalization.
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
Compared to BatchNorm, LayerNorm's performance is not affected by batch size. It is a key component of transformer models. For a given data input and layer, LayerNorm computes the mean μ {\displaystyle \mu } and variance σ 2 {\displaystyle \sigma ^{2}} over all the neurons in the layer.
In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces "noise" into the process, using the local gradient calculated from one data point; this reduces the chance of the network getting stuck in local minima.
Publication timeline of some knowledge graph embedding models. In red the tensor decomposition models, in blue the geometric models, and in green the deep learning models. RESCAL [15] (2011) was the first modern KGE approach. In [16] it was applied to the YAGO knowledge graph. This was the first application of KGE to a large scale knowledge graph.
The Chinchilla team recommends that the number of training tokens is twice for every model size doubling, meaning that using larger, higher-quality training datasets can lead to better results on downstream tasks. [5] [6] It has been used for the Flamingo vision-language model. [7]
It was trained by AdamW optimizer with gradient norm clipping and a linear learning rate decay with warmup, with batch size 256 segments. Training proceeds for 1 million updates (2-3 epochs). No data augmentation or regularization, except for the Large V2 model, which used SpecAugment, Stochastic Depth, and BPE Dropout.
Mean-field limit analysis, when applied to neural networks with weight scaling of / instead of / and large enough learning rates, predicts qualitatively distinct nonlinear training dynamics compared to the static linear behavior described by the fixed neural tangent kernel, suggesting alternative pathways for understanding infinite-width networks.