Machine Learning Fundamentals: dimensionality reduction
dev.to·12h·
Discuss: DEV
📐Linear Algebra
Functional Abstract Interpretation
simon.peytonjones.org·2d·
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🔗Functional Compilers
Revisiting k-Means: 3 Approaches to Make It Work Better
machinelearningmastery.com·13h
📊Vector Quantization
Information Must Flow: Recursive Bootstrapping for Information Bottleneck in Optimal Transport
arxiv.org·1d
⧗Information Bottleneck
A Minimal DDPM
github.com·1d·
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🧠Machine Learning
2025-07-16: Understanding Hallucination in Large Language Models: Challenges and Opportunities
ws-dl.blogspot.com·3h·
Discuss: ws-dl.blogspot.com
✨Effect Handlers
Rethinking Distributed Computing for the AI Era
cacm.acm.org·8h
⚡Incremental Computation
Effectively Zero-Knowledge Proofs for NP with No Interaction, No Setup
eccc.weizmann.ac.il·1d·
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🎯Interactive Provers
My favorite use-case for AI is writing logs
vickiboykis.com·1d
🌳Incremental Parsing
News for June 2025
ptreview.sublinear.info·2d
🕸️Graph Algorithms
Compressed data structures for Heegaard splittings
arxiv.org·23h
🕳️Persistent Homology
What can we expect of LLMs as Software Engineers?
chelseatroy.com·2d
📝Text Parsing
On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization
arxiv.org·1d
🧠Machine Learning
How AI Detects Cancer in Whole Slide Images
hackernoon.com·8h
🧠Machine Learning
From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment
towardsdatascience.com·1d
🧮Kolmogorov Bounds
Categorical Features: The Backbone of Search & Recs Engineering
shaped.ai·2d
🧠Learned Indexing
PLEX: Perturbation-free Local Explanations for LLM-Based Text Classification
arxiv.org·23h
💻Local LLMs
Cheating? Or the acumen of modern programming? FOSS, "AI", and human conscience
gist.github.com·1h·
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🔗Concatenative Programming
Types That Count: Journey across Qualitative and Quantitative Intersection Types
iris.unito.it·2d·
Discuss: Hacker News
🔍Type Inference
Class-Proportional Coreset Selection for Difficulty-Separable Data
arxiv.org·23h
🧠Machine Learning