← Înapoi la Urmăritori

Cel mai recent articol

Ultima verificare

Urmăritori 0 urmăritori
Articole: 30
  1. Abstract This article is about how to use LLMs as an approximate joint probability distribution over tokens rather than as an expert system.

  2. Differentiable Memory

    Introduction Before deep learning was a thing, if you had \(N\) vectors, and you wanted to match the closest one to some noisy version you would use kNN (\(k\) nearest neighbour).

  3. Implementation code is available here.

  4. Snakes & ladders is a classic board game, originally imported into the United Kingdom from India circa 1890 according to Wikipedia.

  5. Introduction The pyevidence repository, installation and usage examples are available here.

  6. Here is an interesting scheme I encountered in the wild, generalised and made abstract for you, my intrepid reader.

  7. Here are a few practical questions of my own invention which are easy to comprehend but very difficult to solve without statistical reasoning competence.

  8. Summary I present a weak supervision paradigm called “data programming” which uses maximum likelihood estimation to produce soft labels from heuristics.

  9. SUMMARY I use 500 Hacker News (HN) titles and an LLM to derive an article ranking model from a user supplied preference description.

  10. INTRODUCTION This post is about sizing independent simultaneous bets through methods related to the Kelly criterion.

  11. A basic application of linear methods are linear regression models.

  12. I read this paper a while ago, which sets out the problem of linear metric learning nicely.

  13. SUMMARY I tackle 1BRC in Fortran which requires processing 1B rows of weather station data (~15GB) to obtain min/max/mean for each station as quickly, as you can muster.

  14. Here are some Advent of Code solutions: 2023 (Prolog) 2022 (Haskell) 2021 (Python & Scala) (in progress at the time of writing).

  15. INTRODUCTION [If you want to have a go straight away, jump to the examples at the bottom of this post.]

  16. What follows are some notes about a minimal proof-of-concept for a stochastic simulator in Prolog via a meta-interpreter.

  17. INTRODUCTION Gentle reader, permit me to try and convince you that data analysis is better with logic programming.

  18. Summary Some notes about the construction of a Block Dominoe playing algorithm for a hidden information variant of the game.

  19. SUMMARY I parse HackerNews (HN) “Ask HN: Who is hiring?”

  20. Introduction I happened upon a post by Gwern discussing, in some detail, various solutions to riddle #14 from Nigel Coldwell’s list of quant riddles.

  21. Introduction I had recently started going to a new specialist gym that runs 3 classes per day during the working week and is closed the rest of the time.

  22. Summary I explain blocking, optimal design and covariate adjustment as methods to improve power in design of experiments.

  23. Summary I motivate clustering as a problem well suited to logic programming in the general case, and I volunteer a couple artisanal clustering algorithms in Prolog demonstrated on some mock data.

  24. Prolog for data science

    Summary I demonstrate a widely applicable pattern which integrates Prolog as a critical component in a data science analysis.

  25. Introduction I often work with clients who have large “data lakes” or big star schema style enterprise databases with fact and dimension tables as far as the eye can see.

  26. Introduction “Data science” has a handful of fundamental metaphors for problem solving, few moreso versatile than the “point cloud”.

  27. SUMMARY I show how assumptions about price structure can be used to build a compelling fixed effect (deterministic) price imputation model for the UK residential housing market.

  28. SUMMARY I use lichess.org games data to investigate the extent to which fast thinking is the dominant factor affecting game outcomes at any time control.

  29. I spent about 50 hours making things with a language called J.

  30. INTRODUCTION The crux with IQ – so far as I understand it – is that performance across abstract reasoning tasks is correlated no matter what the tasks are.