The transformer architecture is notoriously inefficient when processing long sequences — a problem in processing images, which are essentially long sequences of pixels. One way around this is to break up input images and process the pieces
Google will upgrade its search engine with a new model that tracks the relationships between words, images, and, in time, videos — the first fruit of its latest research into multimodal machine learning and multilingual language modeling.
The transformer architecture is notoriously inefficient when processing long sequences — a problem in processing images, which are essentially long sequences of pixels. One way around this is to break up input images and process the pieces
The paper that introduced the transformer famously declared, “Attention is all you need.” To the contrary, new work shows you may not need transformer-style attention at all.What’s new: Hanxiao Liu and colleagues at Google
The paper that introduced the transformer famously declared, “Attention is all you need.” To the contrary, new work shows you may not need transformer-style attention at all.What’s new: Hanxiao Liu and colleagues at Google
Pretrained language models like GPT-3 have shown notable proficiency in few-shot learning. Given a prompt that includes a few example questions and answers (the shots) plus an unanswered question (the task), such models can generate an accurate answer.
Pretrained language models like GPT-3 have shown notable proficiency in few-shot learning. Given a prompt that includes a few example questions and answers (the shots) plus an unanswered question (the task), such models can generate an accurate answer.
A new study examines a major strain of recent research: huge models pretrained on immense quantities of uncurated, unlabeled data and then fine-tuned on a smaller, curated corpus.
A new study examines a major strain of recent research: huge models pretrained on immense quantities of uncurated, unlabeled data and then fine-tuned on a smaller, curated corpus.
Self-attention enables transformer networks to track relationships between distant tokens — such as text characters — in long sequences, but the computational resources required grow quadratically with input size.
Self-attention enables transformer networks to track relationships between distant tokens — such as text characters — in long sequences, but the computational resources required grow quadratically with input size.
The transformer architecture has shown an uncanny ability to model not only language but also images and proteins. New research found that it can apply what it learns from the first domain to the others.
The transformer architecture has shown an uncanny ability to model not only language but also images and proteins. New research found that it can apply what it learns from the first domain to the others.
Object detectors typically detect only items that were labeled in their training data. A new method liberates them to locate and recognize a much wider variety of objects.
Object detectors typically detect only items that were labeled in their training data. A new method liberates them to locate and recognize a much wider variety of objects.
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