transformlike
Transformlike is a term used in artificial intelligence to describe a family of algorithms and architectures that apply a sequence of learned transformations to data representations. The concept emphasizes compositional transformation steps as a way to model complex structure without relying solely on end-to-end attention mechanisms typical of standard transformer models.
Typically, a transformlike system consists of modular transformation blocks that can be linear, nonlinear, convolutional, or
History and development of transformlike ideas trace to AI literature in the 2020s, where the approach was
Applications of transformlike concepts span natural language processing, computer vision, and multimodal tasks. They are used