📄 Text Mining

Document Analysis, Pattern Discovery, Information Extraction, Natural Language Processing

European universities are trying to catch up, as competition for international talent intensifies.
dw.com·1d
🎓Academic Torrents
FreeBSD Security Advisory FreeBSD-SA-25:07.libarchive
bsdsec.net·1d
🐡OpenBSD
Thoughts on teaching multivariable calculus
blog.evanchen.cc·6h
📚Proof Pedagogy
Divine Comedy
thedailywtf.com·1d
🔗Concatenative Programming
AIxCC finals: Tale of the tape
blog.trailofbits.com·2d
🔒WASM Capabilities
Zombie bugs: The parasites that hijack insect minds
english.elpais.com·2h
🦴Database Paleontology
SAT Requires Exhaustive Search
link.springer.com·4d·
Discuss: Hacker News
🧮Kolmogorov Complexity
Show HN: I built a plugin to create a ChatGPT archive with Typemill CMS
typemill.net·2d·
Discuss: Hacker News
🤖Archive Automation
Laser analysis enables industry to map mineral samples at an unprecedented scale
phys.org·1d
🌈Spectroscopy
Introduction to Supervised Machine Learning for Beginners
dev.to·6d·
Discuss: DEV
🧠Machine Learning
**Automated Microvascular Network Reconstruction via Multi-Modal Data Fusion & Graph-Based Analytics**
dev.to·12h·
Discuss: DEV
📄Document Digitization
Winning the Game You Didn’t Choose: The Case for External Cyber Defense in Government and Education
blog.checkpoint.com·1d
🎯Threat Hunting
DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
arxiv.org·5d
🏛Digital humanities
Machine Learning Fundamentals: model evaluation
dev.to·3d·
Discuss: DEV
🌊Stream Processing
The Ultimate List of 50 LLMs Interview Question
dev.to·3d·
Discuss: DEV
💻Local LLMs
Thinking with Nothinking Calibration: A New In-Context Learning Paradigm in Reasoning Large Language Models
arxiv.org·3d
🎯Dependent Parsing
How Deep Is Representational Bias in LLMs? The Cases of Caste and Religion
arxiv.org·2d
🏛Digital humanities
Jeff Atwood on Writing, Optimism, and Fixing the Internet
hackernoon.com·21h
🌱Personal Wikis
Decoupled Contrastive Learning for Federated Learning
arxiv.org·2d
🧠Machine Learning
MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
arxiv.org·1d
🧮SMT Solvers