Learning languages with the help of algorithms
johndcook.com·1d·
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🧮Kolmogorov Bounds
Parallelizable Feynman-Kac Models for Universal Probabilistic Programming
arxiv.org·1d
🧮Kolmogorov Bounds
Latent trees
languagelog.ldc.upenn.edu·4h
🤖Grammar Induction
Thermodynamically Optimized Data Batching via Information-Theoretic Reservoir Sampling in HBM
dev.to·10h·
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🌊Streaming Compression
Worth Reading 091925
rule11.tech·1h
🕸️Mesh Networking
An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers
towardsdatascience.com·2h
🤖Advanced OCR
2025-09-17: Classic Machine Learning Models and XAI Methods
ws-dl.blogspot.com·1d·
🧠Machine Learning
Language Models Pack Billions of Concepts into 12,000 Dimensions
nickyoder.com·4d·
🧠Machine Learning
Emergent Alignment via Competition
arxiv.org·12h
🔲Cellular Automata
Joint Scheduling and Multiflow Maximization in Wireless Networks
arxiv.org·12h
📡Network Calculus
On the complexity of unique quantum witnesses and quantum approximate counting
arxiv.org·12h
⚛️Quantum Circuits
Such a Classic
blog.hermesloom.org·1d·
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🧠Intelligence Compression
Smarter Pixels: Turbocharging Visual AI with Semantic Compression by Arvind Sundararajan
dev.to·22h·
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🧠Neural Compression
Large Language Model probabilities cannot distinguish between possible and impossible language
arxiv.org·12h
💻Programming languages
Building tenets: Intelligent context aggregation for AI pair programming
jddunn.github.io·1d·
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🌀Brotli Internals