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Articles 184,140Blog Posts 167,176Tech Tutorials 49,351Research Papers 35,881News 22,742
⚡ AI Lessons
Dev.to · Jun-Sik Yoo, PhD
🔢 Mathematical Foundations
5d ago
A Pedagogical Introduction to Porting a Conjugate Gradient Solver to CUDA
GPU programming tutorials often begin with isolated examples: vector addition, reductions, matrix...
Medium · LLM
🔢 Mathematical Foundations
5d ago
Linear Algebra: Basics 2# Randomized SVD — Modern Matrix Factorization with speed
Make your Matrix Factorisation faster Continue reading on Medium »
Medium · AI
🔢 Mathematical Foundations
6d ago
Eigenvalues & Eigenvectors — The Special Directions Hidden Inside a Matrix
In the previous articles, we changed the way we look at matrices. Continue reading on Medium »
Medium · Deep Learning
🔢 Mathematical Foundations
6d ago
Eigenvalues & Eigenvectors — The Special Directions Hidden Inside a Matrix
In the previous articles, we changed the way we look at matrices. Continue reading on Medium »
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
6d ago
EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
arXiv:2408.14575v5 Announce Type: replace Abstract: EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialog
Medium · Deep Learning
🔢 Mathematical Foundations
6d ago
The Hidden Cost of Turning Neural Networks Into Probability Distributions
Why the normalizing constant exposes the tension between expressive generative models and tractable probability computation. Continue reading on Medium »
Towards Data Science
🔢 Mathematical Foundations
6d ago
Reparameterization Tricks: Variance Reduction by Smarter Gradients
How moving randomness outside the computation graph turns noisy gradient estimators into low-variance, differentiable ones The post Reparameterization Tricks: V
Medium · Programming
🔢 Mathematical Foundations
⚡ AI Lesson
6d ago
TypeScript Covariance and Contravariance Without the Theory: A Practical Guide to Generic Type…
Most type safety breakdowns in TypeScript stem from misunderstanding variance. Learn how covariance and contravariance protect your… Continue reading on Medium
Towards Data Science
🔢 Mathematical Foundations
6d ago
Seizing the Moment: The Hidden Silhouette of Data
How statistical moments connect the mean, the variance, and higher powers of a distribution The post Seizing the Moment: The Hidden Silhouette of Data appeared
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
1w ago
Gap Entropy and Almost Instance-Wise Optimal Best-Arm Identification
arXiv:2609.13703v1 Announce Type: cross Abstract: In the best-arm identification problem, we are given $n$ stochastic arms with unknown means and wish to identi
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
1w ago
Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression
arXiv:2609.15838v1 Announce Type: cross Abstract: Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius nor
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
1w ago
Constrained Online Learning with Noisy Constraint Values
arXiv:2609.06921v2 Announce Type: replace-cross Abstract: We study constrained online convex optimization with adversarial constraints and conditionally unbiase
Medium · Machine Learning
🔢 Mathematical Foundations
1w ago
What A Matrix Actually Does To Space
If you read the Linear Algebra for AI series, you already know what a vector and a matrix are. This one is about what a matrix actually… Continue reading on Art
Medium · Deep Learning
🔢 Mathematical Foundations
1w ago
What A Matrix Actually Does To Space
If you read the Linear Algebra for AI series, you already know what a vector and a matrix are. This one is about what a matrix actually… Continue reading on Art
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
1w ago
Linear Exponential Quadratic Gaussian Covariance Steering
arXiv:2609.12463v1 Announce Type: cross Abstract: We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in conti
Medium · Machine Learning
🔢 Mathematical Foundations
1w ago
Gradients 1: Backprop Is the Chain Rule, and the Chain Rule Has Hypotheses
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and… Continue reading on Medi
Medium · Data Science
🔢 Mathematical Foundations
1w ago
Gradients 1: Backprop Is the Chain Rule, and the Chain Rule Has Hypotheses
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and… Continue reading on Medi
Medium · LLM
🔢 Mathematical Foundations
1w ago
Gradients 1: Backprop Is the Chain Rule, and the Chain Rule Has Hypotheses
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and… Continue reading on Medi
Towards Data Science
🔢 Mathematical Foundations
1w ago
The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is
Frequentist confidence intervals and Bayesian credible intervals answer different questions, and confusing them can distort product decisions The post The 95% I
Medium · Programming
🔢 Mathematical Foundations
⚡ AI Lesson
1w ago
Lord of the Algebras: Groups, Rings, and Semirings in C#
This article is part of “Abstract Algebra in .NET” Series Continue reading on Medium »
OpenAI News
🔢 Mathematical Foundations
⚡ AI Lesson
1w ago
On the Navier–Stokes Millennium Prize Problem
We’re sharing an AI-generated solution to the Navier–Stokes Millennium Prize Problem, including a writeup and a formal proof in Lean.
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
3w ago
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
arXiv:2608.28853v1 Announce Type: cross Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-ord
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
3w ago
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
arXiv:2608.29507v1 Announce Type: cross Abstract: Diffusion models are increasingly used not only for sampling from learned data distributions, but also for gen
ArXiv cs.AI
🔢 Mathematical Foundations
📄 Paper
3w ago
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
arXiv:2505.22973v3 Announce Type: replace-cross Abstract: Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration
DeepCamp AI