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No recurrent units
- Transformers have the advantage of having no recurrent units, and therefore require less training time than earlier recurrent neural architectures such as long short-term memory (LSTM).
en.wikipedia.org › wiki › Transformer_(deep_learning_architecture)
Apr 7, 2020 · It's because of the path length. If you have a sequence of length n. Then a transformer will have access to each element with O(1) sequential operations where a recurrent neural network will need at most O(n) sequential operations to access an element.
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Transformers have the advantage of having no recurrent units, and therefore require less training time than earlier recurrent neural architectures such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets, such as the Wikipedia corpus and Common Crawl .
May 28, 2024 · The trend of transformers surpassing recurrent neural networks (RNNs) continued in 2020: Vision Transformer (ViT, 2020): This model demonstrated that transformers could outperform RNNs in the realm of computer vision tasks like image recognition.
Jul 6, 2022 · Block-Recurrent Transformer is a novel Transformer model that leverages the recurrence mechanism of LSTMs to achieve significant perplexity improvements in language modeling tasks over long-range sequences.
Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have been used to deal with this problem because of their properties. Let’s go over these two architectures and their drawbacks.
A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder.
May 24, 2024 · An RNN (recurrent neural network) processes sequences step-by-step or sequentially. A transformer uses self-attention mechanisms to process sequences in parallel, meaning multiple different parts of a sequence are processed at the same time.
