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robertjeffrey1236/Radial-Hyperbolic-Architecture-Prototype: Python prototype for efficient hierarchical computing in Poincaré disk. Features dual Φ spirals, fractal recursion, lazy loading, multi-point observers with portals, Φ percolation for Penrose shadows, atomic scaling with wormholes/phonons, and music substrate via JI harmonics. Simulates emergent patterns on bounded resources.
github.com·41m·
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💎Information Crystallography
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Learning to Solve PDEs on Neural Shape Representations
arxiv.org·2d
🌀Riemannian Computing
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Algebraic Topology 1: Homotopy Equivalence
youtube.com·12h
🕸️Algebraic Topology
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Treating Functions as Vectors in Hilbert Space
hackaday.com·2d
📐Linear Algebra
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Deep Convolutional Networks on Graph-Structured Data
paperium.net·2d·
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📊Spectral Graphs
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Book Review: Why Machines Learn
philippdubach.com·1d·
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🧠Machine Learning
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You Do NOT need augmentations to train your Classifiers!!
dev.to·14h·
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📊Learned Metrics
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Natural language processing for word sense disambiguation and information extraction
arxiv.org·19h·
Discuss: r/compsci
📥Feed Aggregation
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Keeping Probabilities Honest: The Jacobian Adjustment
towardsdatascience.com·2d
🎯Arithmetic Coding Theory
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Gaussian concentration bounds for probabilistic cellular automata | Journal of Statistical Physics
link.springer.com·1d
🔲Cellular Automata
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Quadratrix of Hippias
en.wikipedia.org·23h·
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📐Projective Geometry
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Port of Statistical Rethinking (2nd edition) code to Julia
shmuma.github.io·6h·
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λLambda Formalization
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Courses with certificates
codefinity.com·20h
🧠Neural Codecs
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Cross-Modal Knowledge Distillation for deep-sea exploration habitat design with embodied agent feedback loops
dev.to·1d·
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🔲Cellular Automata
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$\text{H}^2$em: Learning Hierarchical Hyperbolic Embeddings for Compositional Zero-Shot Learning
arxiv.org·3d
🌀Hyperbolic Geometry
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Analytic and Variational Stability of Deep Learning Systems
arxiv.org·2d
🌀Riemannian Computing
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Pokémon Team Optimization
nchagnet.pages.dev·2d·
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📈Linear programming
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Easily Dealing With Any-Dimensional Planes
gpfault.net·3d·
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📐Projective Geometry
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Stanford CS 224N | Natural Language Processing with Deep Learning
web.stanford.edu·1d
🧠Machine Learning
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wwes4/AI_Accel_1.5x: AI acceleration framework for ~1.5x speedups in mid-sized models via tension-based pruning. Built utilizing xAI's Grok.
github.com·1d·
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📊Quantization
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