Gradient Descent, Convex Optimization, Stochastic Methods, Loss Functions

Failure Is Required
theaiunderwriter.substack.com·15h·
Discuss: Substack
🔶TensorFlow
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Bigger datasets aren't always better
news.mit.edu·3d·
🎯Optimization Theory
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AI/ML for Biology and Healthcare: A Learning Path
iamtk.co·4d·
📊Data Science
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Dominance: The Standard Everyday Solution To Akrasia
lesswrong.com·9h
🎮Reinforcement Learning
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Artificial Intelligence and Accounting Research: A Framework and Agenda
arxiv.org·2h
🤖AI
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A CUR Krylov Solver for Large-Scale Linear Matrix Equations
arxiv.org·2d
📐Linear Algebra
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Graded strength of comparative illusions is explained by Bayesian inference
arxiv.org·2d
🎲Probability Theory
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Supervised Contrastive Learning for Few-Shot AI-Generated Image Detection and Attribution
arxiv.org·2h
Automatic Differentiation
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A time for monsters: Organizational knowing after LLMs
arxiv.org·2h
🗣️Large Language Models
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Differentiable Sparse Identification of Lagrangian Dynamics
arxiv.org·4d
Automatic Differentiation
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MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering
arxiv.org·2h
🗣️Large Language Models
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H-CNN-ViT: A Hierarchical Gated Attention Multi-Branch Model for Bladder Cancer Recurrence Prediction
arxiv.org·2d
🔥PyTorch
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Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer
arxiv.org·1d
🧠Deep Learning
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