Single-pass Adaptive Image Tokenization for Minimum Program Search | What's the Kolmogorov Complexity of an Image?
Python implementation of block decomposition method for approximating algorithmic complexity
Minimum Description Length Recurrent Neural Networks (MDLRNNs) in PyTorch
An Algorithmic Information Theory Library (And Information-theory broadly); Implements numerous approximations, estimations, as well as a comprehensive MC-AIXI Agent & Model Ensemble platform.
Repository for the Online Algorithmic Complexity Calculator
Minimum Description Length Recurrent Neural Networks
Repository for the Online Algorithmic Complexity Calculator
(TODO) actually callable javascript lambdas of infinitely threadable (potentially massively-multiplayer) godel-like-numbering-secure low latency neuralnet approximation of hypercomputation of the iota combinator sparse-emulating debugger breakpoints of itself, in javascript lambdas, such as naming every possible lambda by a 256 bit merkle id
Autonomous Agent Normalized Compression Distance (NCD) & Kolmogorov Complexity Universal Metric
Autonomous Agent Normalized Compression Distance (NCD) & Kolmogorov Complexity Universal Metric
Computational predictions of protein attributes associated with COVID-19 using Data Science techniques
Estimate the Kolmogorov complexity of grayscale images and weighted graphs
Estimation of the robustness of networks to attacks directed using Kolmogorov complexity as estimated by the Block Decomposition Method.
A small Julia library for calculating the normalized compression distance.
Minimum Description Length Hopfield Networks
Introducing Lossless Agentic Compression. We introduce a method, and codebase, for losslessly compressing files using (generative) AI Agents, and explore the implications that come with this. Furthermore, we propose a definition and generalizable measure of intelligence, extending classic works.
Biney's Procedural Compression - A research proposal exploring lossless compression as a search problem: finding the smallest procedural description (algorithm + seed) that reconstructs the original data exactly.
In Kolmogorov's sense of complexity, conditional complexity allows to take some background knowledge into account for description complexity. Can this concept be applied to image recognition? I did a quick experiment to try to highlight this process in the context of image recognition.
Python implementation of block decomposition method for approximating algorithmic complexity
Python code to implement an efficient approach to unsupervised OOD detection with VAE
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