#attention-mechanisms

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fromHackernoon
1 year ago

Defining the Frontier: Multi-Token Prediction's Place in LLM Evolution | HackerNoon

Dong et al. (2019) and Tay et al. (2022) train on a mixture of denoising tasks with different attention masks (full, causal and prefix attention) to bridge the performance gap with next token pretraining on generative tasks.
Artificial intelligence
fromHackernoon
1 year ago

In Cancer Research, AI Models Learn to See What Scientists Might Miss | HackerNoon

Multi-instance learning with attention mechanisms effectively identifies tumor regions, but TP53 mutation detection remains more complex and less accurate.
#large-language-models
fromHackernoon
1 month ago
Artificial intelligence

Issues with PagedAttention: Kernel Rewrites and Complexity in LLM Serving | HackerNoon

fromHackernoon
1 month ago
Artificial intelligence

Issues with PagedAttention: Kernel Rewrites and Complexity in LLM Serving | HackerNoon

more#large-language-models
#transformers
fromMedium
3 months ago
Artificial intelligence

Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention enhances transformers by allowing simultaneous focus on groups of tokens, improving contextual understanding.
Traditional attention considers one token at a time, limiting interaction capture among tokens.
fromMedium
3 months ago
Artificial intelligence

Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention revolutionizes transformers by enabling simultaneous attention to groups of tokens, enhancing contextual understanding.
Artificial intelligence
fromMedium
3 months ago

Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention enhances transformers by allowing simultaneous focus on groups of tokens, improving contextual understanding.
Traditional attention considers one token at a time, limiting interaction capture among tokens.
Artificial intelligence
fromMedium
3 months ago

Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention revolutionizes transformers by enabling simultaneous attention to groups of tokens, enhancing contextual understanding.
more#transformers
Artificial intelligence
fromMedium
3 months ago

Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention allows transformers to attend to groups of tokens, enhancing model performance in natural language processing.
Artificial intelligence
fromHackernoon
4 months ago

Linear Attention and Long Context Models | HackerNoon

The article explores advancements in selective state space models, enhancing efficiency and effectiveness in tasks like language modeling and DNA analysis.
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