Hackernews posts about Transformer
Transformer is a type of neural network architecture designed for natural language processing tasks that relies on self-attention mechanisms to process input sequences in parallel.
Related:
Apple
- The Illustrated Transformer (jalammar.github.io)
- Transformer Paper Authors at AI Startup Debut Open Source Model (www.bloomberg.com)
- Stronger Normalization-Free Transformers (arxiv.org)
- The Bayesian Geometry of Transformer Attention (arxiv.org)
- The Transformer as Renormalization Group Flow (www.symmetrybroken.com)
- Can a Transformer "Learn" Economic Relationships? (aleximas.substack.com)
- Understanding Attention in Transformers with Visual Intuition (miladvlp.github.io)
- Transformers Must Hallucinate (medium.com)
- Transformers Are Multi-State RNNs (huggingface.co)
- Transformer Architecture Visualizer (weavers.neocities.org)
- Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers [pdf] (users.cs.utah.edu)
- Transformer Paper Authors Debut Open Source Model (www.bloomberg.com)
- The Annotated Transformer (2018) (nlp.seas.harvard.edu)
- Shaping the future of AI from the history of Transformer [2024] (docs.google.com)
- Bayesian Geometry of Transformer Attention (arxiv.org)
- Transformers in Action (www.manning.com)
- Can a Transformer "Learn" Economic Relationships? Revisiting the Lucas Critique (aleximas.substack.com)
- From a For-Loop to Transformers (python2llms.org)