[1706.03762] Attention Is All You Need - arXiv.org
2017年6月12日 · We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Attention Is All You Need - Wikipedia
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
Attention is All You Need - Google Research
We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Byte Latent Transformer: Patches Scale Better Than Tokens
6 天之前 · We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on the entropy of the next byte ...
Causal Diffusion Transformers for Generative Modeling
2 天之前 · View a PDF of the paper titled Causal Diffusion Transformers for Generative Modeling, by Chaorui Deng and 4 other authors. View PDF HTML (experimental) Abstract: We introduce Causal Diffusion as the autoregressive (AR) counterpart of Diffusion models. It is a next-token(s) forecasting framework that is friendly to both discrete and continuous ...
A comprehensive survey on applications of transformers for deep ...
2024年5月1日 · To address the absence of such a survey, this paper presents a comprehensive analysis of all Transformer-based models, and identifies the top five application fields, namely NLP, CV, Multi-Modality, Audio & Speech, and Signal Processing, and proposes a taxonomy of Transformer models, with significant models being classified and analyzed based ...
Transformer: A Novel Neural Network Architecture for Language …
2017年8月31日 · In our paper, we show that the Transformer outperforms both recurrent and convolutional models on academic English to German and English to French translation benchmarks. On top of higher translation quality, the Transformer requires less computation to train and is a much better fit for modern machine learning hardware, speeding up training by ...
[2302.07730] Transformer models: an introduction and catalog
2023年2月12日 · The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models. The paper also includes an introduction to the most important aspects and innovations in Transformer models.
An Overview of Transformers - Papers With Code
Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.
Transformer Explained - Papers With Code
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.