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In order to get a smaller model, one often uses larger model, that performs reasonably well on the data as a teacher, and uses the information from large model to train the smaller one. There are seve …

The purpose of introduction positional embeddings to the Transformer is clear - since in the original formulation Transformer is equivariant to permutations of tokens, and the original task doesn't respect …

Transformer architecture (without position embedding) is by the very construction equivariant to the permutation of tokens. …

layers have 4 learnable tensors (in the vanilla formulation): Query matrix $W_Q$ Key matrix $W_K$ Value matrix $W_V$ Output matrix $W_O$ Nice illustration from https://jalammar.github.io/illustrated-transformer …

Positional encoding (PE) is an essential part of the self-attention layers in the transformer architectures since without adding it in some way (fixed of learnable) to the input embeddings model has ultimately …

In the problems of NLP and sequence modeling, the Transformer architectures based on the self-attention mechanism (proposed in Attention Is All You Need) have achieved impressive results and now are the …

And the more recent transformer models have the ability to choose which of the neighboring data properties is more important for its output. …

In the famous work on the Visual Transformers, the image is split into patches of a certain size (say 16x16), and these patches are treated as tokens in the NLP tasks. In order to perform classificati …