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Sources (3)

Attention Is All You Need: A Survey of Transformer Architectures

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2024Research Paper1,247 citations95% Relevance

TL;DR: Comprehensive survey analyzing transformer architecture evolution from the original attention mechanism to modern efficient variants, covering 200+ papers across NLP, vision, and multi-modal domains.

Efficient Transformers: Compute-Optimal Scaling Laws

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2023Research Paper892 citations88% Relevance

TL;DR: Revisits Chinchilla-style scaling laws for sparse mixture-of-experts models, showing training-compute efficiency gains across 30+ model scales.

Vision Transformers for Dense Prediction Tasks

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2023Research Paper534 citations82% Relevance

TL;DR: Adapts the ViT backbone for segmentation and depth estimation, outperforming convolutional baselines on ADE20K and NYU-Depth v2 benchmarks.

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Attention Is All You Need

2017Research Paper1,247 citations

A. Vaswani, N. Shazeer, N. Parmar et al.

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Abstract

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. We propose a new simple architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

Figure 1. Transformer — model architecture.

Key Findings

Self-attention layers replace recurrence for sequence modeling
Multi-head attention integrates information from different subspaces
Achieves 28.4 BLEU on WMT 2014 English-to-German translation

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Attention Is All You Need

2017Research Paper1,247

A. Vaswani, N. Shazeer et al.

Self-attention is a mechanism relating different positions of a single sequence to compute a representation. The Transformer is the first transduction model relying entirely on self-attention.

The Transformer allows for significantly more parallelization and can be trained in as little as twelve hours on eight P100 GPUs — a fraction of the time required by recurrent models.

On the WMT 2014 English-to-German translation task, the big Transformer model establishes a new single-model state-of-the-art BLEU score of 28.4.

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The Transformer models long-range dependencies with self-attention1.2, trains much faster than recurrent architectures1.4, and reaches 28.4 BLEU on WMT 2014 English–German1.7.

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