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Transformers

Known as: Transformers: Hunt for the Decepticons, Transformers: Rise of the Dark Spark - The Album, Transformers (disambiguation) 
Transformers (Japanese: トランスフォーマー Hepburn: Toransufōmā) is a media franchise produced by American toy company Hasbro and Japanese toy company Takara… 
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Related topics

Related topics

18 relations
Artificial consciousnessArtificial intelligence in fictionAsus Eee Pad Transformer PrimeBlocksworld

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2020
Highly Cited
2020
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection… 
Highly Cited
2020
Highly Cited
2020
Transformers: State-of-the-Art Natural Language Processing
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining… 
Highly Cited
2020
Highly Cited
2020
Big Bird: Transformers for Longer Sequences
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of… 
Highly Cited
2019
Highly Cited
2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from… 
Highly Cited
2019
Highly Cited
2019
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining… 
Highly Cited
2019
Highly Cited
2019
Generating Long Sequences with Sparse Transformers
Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this… 
Highly Cited
2019
Highly Cited
2019
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the… 
Highly Cited
2015
Highly Cited
2015
Spatial Transformer Networks
Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to… 
Review
2000
Review
2000
Monolithic transformers for silicon RF IC design
  • J. Long
  • IEEE Journal of Solid-State Circuits
  • 2000
  • Corpus ID: 14476646
A comprehensive review of the electrical performance of passive transformers fabricated in silicon IC technology is presented… 
Highly Cited
1995
Highly Cited
1995
Monad transformers and modular interpreters
We show how a set of building blocks can be used to construct programming language interpreters, and present implementations of… 
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