From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
arxiv.org·4d
🧠Neural Codecs
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Intro to Routing: Mixture-of-Experts and Expert Choice
neelsomaniblog.com·1d·
Discuss: Hacker News
🧮Kolmogorov Bounds
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🔥 LLM Interview Series(5): Self-supervised Learning and Next-token Prediction
dev.to·16h·
Discuss: DEV
🤖Grammar Induction
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AMD vs. Intel: a Unicode benchmark
lemire.me·6h
🇨🇳Chinese Computing
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Aligning machine and human visual representations across abstraction levels
nature.com·3d
📊Learned Metrics
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EyesOff: I Built a Screen Contact Detection Model
ym2132.github.io·23h·
Discuss: Hacker News
📊Learned Metrics
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TSU 101 an New Type of Computing Hardware
extropic.ai·8h·
Discuss: Hacker News
Homebrew CPUs
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Brand Tagging with VLMs
dev.to·10h·
Discuss: DEV
🕸️WebP Analysis
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CDT in NLP Distinguished Lecture Series: Dan Roth
informatics.ed.ac.uk·1d
🎭Cultural Informatics
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Teaching AI to see the world more like we do
deepmind.google·4h·
Discuss: Hacker News
🔲Cellular Automata
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ML Systems Textbook by Havard
mlsysbook.ai·6h·
Discuss: Hacker News
🧠Machine Learning
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Lossless Compression with Asymmetric Numeral Systems (2020)
bjlkeng.io·5d·
Discuss: Hacker News
🎯Arithmetic Coding
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I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
towardsdatascience.com·17h
📊Learned Metrics
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
arxiviq.substack.com·2d·
Discuss: Substack
🧠Machine Learning
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Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
arxiv.org·2d
🧠Machine Learning
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How AI-Generated Content Is Polluting the Internet: Detection and Solutions
dev.to·3h·
Discuss: DEV
🤖AI Curation
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Goodbye *ibe Coding
ikouchiha47.github.io·7h·
Discuss: Hacker News
🌀Brotli Internals
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Split-Layer: Enhancing Implicit Neural Representation by Maximizing the Dimensionality of Feature Space
arxiv.org·2d
🧠Machine Learning
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Building a simple RAG system in PHP with the Neuron AI framework in one evening
dev.to·10h·
Discuss: DEV
🦀Rust Macros
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Continuum Dropout for Neural Differential Equations
arxiv.org·2d
📊Quantization
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