Md. Asif Uddin

The commonplace book

Marginalia

Notes written in the margins of the work: what I thought of a book, what a model actually does when you use it, and the occasional argument that did not fit anywhere else. Kept in one ledger, numbered as they are written.

  1. modelGeneformerThe model is fine. The framing wasn'tWhat Geneformer actually is Geneformer is a bidirectional encoder, BERT in shape, pretrained on Genecorpus-30M and later extended to around 95 million cells. The interesting design decision is the input representation. Single-cell expression data is a problem for tokenization. Counts are sparse, noisy, and vary in scale between cells for reasons that have nothing to do with biology. Feeding raw values invites the model to learn sequencing depth. Geneformer's answer is rank value encoding. Within each cell, genes are ranked by expression, but normalized first by that gene's median expression across the entire corpus. A gene that is highly expressed everywhere gets demoted. A gene that is unusually high in this particular cell rises. The result is a ranked list per cell where position encodes something closer to cell identity than to library size. Then the standard masked objective. Hide some genes in the ranking, predict them from the rest.
  2. bookElements of Causal Inference (Peters, Janzing and Scholkopf, 2017)The Machine Learning Guide to CausalityMIT Press, 2017. It's open access, so the PDF is free, and there are companion Jupyter notebooks with coding exercises. This book connects machine learning with causal reasoning. It shows how algorithms can figure out cause and effect just by looking at regular data.
  3. bookCausal Inference in Statistics: A Primer (Pearl, Glymour and Jewell, 2016)The Starter Guide to Causal InferenceWiley, 2016. Pearl with Madelyn Glymour and Nicholas Jewell, a Berkeley biostatistician, and it shows. This is the only one of the three that behaves like a textbook. This book is a beginner-friendly guide to understanding cause and effect. It uses clear examples and simple math to help you build models and solve real-world problems.
  4. bookCausality: Models, Reasoning, and Inference (Judea Pearl, 2009)Beyond Correlation: A Review of Pearl’s CausalityCambridge University Press, 2nd edition 2009, first published 2000. Eleven chapters, four hundred-odd pages, no attempt to be charming. This book helps AI and statistics move past simple correlation to understand true cause and effect. Pearl provides math tools to help readers map relationships, predict outcomes, and test "what if" scenarios.
  5. modelSigLIP 2SigLIP 2Models I actually use, and what annoys me about them.
  6. noteDataset acquisition as a research methodOn asking thirty-seven timesEvery hospital said no. One research group said yes. That is the dataset.
  7. modelDINOv3 as a frozen backboneWhat the registers fixedDense features that no longer need apologising for.
  8. bookJudea Pearl & Dana Mackenzie — The Book of WhyThe ladder is the argumentA polemic disguised as a popular science book, and better for it.