Variable-Width Transformers (><former)

New Method Enables Constant-Cost Self-Attention Computation for Transformers

Researchers present a novel mathematical approach to compute self-attention in Transformer AI models with constant cost per token, rather th

Analyzing Positional Encodings in Transformer Models: Impact on Expressiveness and Generalization

This paper presents a theoretical analysis of various positional encoding methods in transformer models, focusing on their impact on express

Hugging Face Transformers Guide for AI Engineers

Tauformer: A Topological Transformer Architecture Using Laplacian-Derived Scalar Attention

The article discusses Tauformer, a novel topological transformer architecture that replaces traditional dot-product attention with a Laplaci

Exploring FORTH and Associative Languages as Better Fits for Transformer Architectures

The article explores the idea that FORTH and associative/applicative programming languages might be better suited for transformer architectu

Transformers Can Learn to Predict Permuted Congruential Generator Sequences Through Curriculum Learning and Scaling Laws

This research paper investigates whether Transformer models can learn to predict sequences generated by Permuted Congruential Generators (PC