Learning to Learn

Poly-Attention — A Better Attention Mechanism That's Still Fast Poly-Attention — A Better Attention Mechanism That's Still Fast Most improvements to the Transformer's self-attention mechanism work at the level of approximation — finding faster ways to compute the same pairwise dot products. Sparse attention, linear attention, flash attention: all of these keep the fundamental computation the same and just make it cheaper. They share an assumption: pairwise token interactions are the right thing to compute, and the goal is to do it faster. A recent paper from Columbia University — Poly-Attention: A General Scheme for Higher-Order Self-Attention (Chakrabarti, Pitassi, Alman, 2026) — makes a different move. It asks whether pairwise interactions are expressive enough , proves they often aren't, and then builds a unified mathematical framework for richer attention mechanisms. The punchline is a new mechanism called tree-attention that is simultaneously ...

End-to-End Medical Image AI: From Raw DICOM to Clinical Deployment End-to-End Medical Image AI: From Raw DICOM to Clinical Deployment There is a question worth sitting with before writing a single line of code: when a radiologist reads a CT scan, they do not run three separate mental programs — one for loading the DICOM files, another for deciding which Hounsfield window to apply, a third for actually spotting the tumour. Something more practised happens. A trained visual system and decades of anatomical knowledge combine seamlessly into a single interpretive act. The pixels become a diagnosis. Building a machine that replicates even a fraction of that process requires solving a surprising number of sub-problems in the right order. This post walks through every stage of the pipeline — from where to get publicly available MRI and CT data, through preprocessing, model architecture, loss functions, training mechanics, and all the way to clinical deployment and ongoing mon...