In machine learning, an easy task and a more difficult version of the same task — say, a maze that covers a smaller or larger area — often are learned separately.
The process known as image-to-image style transfer — mapping, say, the character of a painting’s brushstrokes onto a photo — can render inconsistent results. When they apply the styles of different artists to the same target
The process known as image-to-image style transfer — mapping, say, the character of a painting’s brushstrokes onto a photo — can render inconsistent results. When they apply the styles of different artists to the same target
Playing a team sport involves a fluid blend of individual and group skills. Researchers integrated both types of action into realistic humanoid agents that play football (known as soccer in the U.S.).
Playing a team sport involves a fluid blend of individual and group skills. Researchers integrated both types of action into realistic humanoid agents that play football (known as soccer in the U.S.).
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.
Pretraining methods generate basic representations for later fine-tuning, but they’re prone to certain issues that can throw them off-kilter. New work proposes a solution.
Pretraining methods generate basic representations for later fine-tuning, but they’re prone to certain issues that can throw them off-kilter. New work proposes a solution.
The previous state-of-the-art model for playing vintage Atari games took advantage of a number of advances in reinforcement learning (RL). The new champion is a basic RL architecture plus a trick borrowed from image generation.
The previous state-of-the-art model for playing vintage Atari games took advantage of a number of advances in reinforcement learning (RL). The new champion is a basic RL architecture plus a trick borrowed from image generation.
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