#attention
10 articles
- A Simple Neural Network Module for Relational Reasoning Relation Networks for learning to reason about object relationships
- Attention Is All You Need The 2017 paper that introduced the Transformer architecture
- Neural Machine Translation by Jointly Learning to Align and Translate The paper that introduced the attention mechanism for sequence-to-sequence models
- Neural Turing Machines Neural networks augmented with external memory and attention-based read/write heads
- Order Matters: Sequence to Sequence for Sets How input and output ordering affects seq2seq learning on set-structured data
- Pointer Networks Neural architecture that outputs pointers to input positions, enabling variable-size outputs
- Relational Recurrent Neural Networks RNNs with relational memory that enables reasoning across time
- The Annotated Transformer Line-by-line PyTorch implementation of the Transformer architecture
- Transformer Self-attention models that process sequences in parallel
- Vision Transformer (ViT) Applying Transformers directly to image patches for visual recognition