Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
arxiv.org·16h
🌀Riemannian Computing
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Scalable Knowledge Graph Embedding via Adaptive Dimensionality Reduction & Multi-Objective Optimization
🕸️Graph Embeddings
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Why wavelets is a bad choice for image coding
codecs.multimedia.cx·1d
📊Rate-Distortion Theory
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Vectorizing for Fun and Performance
⚡SIMD Vectorization
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Empirical Bayesian Multi-Bandit Learning
arxiv.org·16h
🧮Kolmogorov Bounds
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Developing a Multi-task Ensemble Geometric Deep Network for Supply Chain Sustainability and Risk Management
arxiv.org·16h
🌀Riemannian Computing
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MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction
arxiv.org·16h
🧠Machine Learning
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Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data
arxiv.org·3d
🌀Riemannian Computing
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Building a Rules Engine from First Principles
towardsdatascience.com·1d
🔗Combinatory Logic
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Unlocking Neural Network Secrets: The Geometric Awakening by Arvind Sundararajan
🌀Differential Geometry
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Equation Discovery, Parametric Simulation, and Optimization Using the Physics-Informed Neural Network (PINN) Method for the Heat Conduction Problem
arxiv.org·16h
🧠Machine Learning
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TOPol: Capturing and Explaining Multidimensional Semantic Polarity Fields and Vectors
arxiv.org·1d
🧮Vector Embeddings
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Show HN: Hot or Slop – Visual Turing test on how well humans detect AI images
📊Learned Metrics
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