Leveraging network motifs to improve artificial neural networks

➗ This work presents a thorough analysis of a quadratic optimization model, confirming convexity, coercivity, and the exact global minimum. It compares gradient descent and Newton’s method, highlighting Newton’s superior efficiency when the Hessian is invertible.

Research project analyzing stability and robustness of deep learning optimizers (SGD, Adam, SAM) under label noise and precision constraints.

Detect numerical instability in ML applications using learned invariants (Soft Assertions) without modifying model logic. ACM FSE 2025.

Makine Öğrenmesi için Lineer Cebir ve Matris Hesaplamaları masterclass'ı. Hazır kütüphaneler yok; saf geometrik sezgi ve NumPy var. Sıfırdan Gauss eleme, LU ve QR ayrıştırmaları, kararlı en küçük kareler, özdeğer iterasyonları ve doruk noktası olarak PCA ile uçtan uca SVD tabanlı boyut indirgeme mimarisi.

R package and replication code for the article “Numerical stability enhancements in beta autoregressive moving average model estimation” by Cribari-Neto, F., Costa, E., and Fonseca, R. V., published in the Brazilian Journal of Probability and Statistics (2025). DOI: 10.1214/25-BJPS645.

Pre-registered experiment on differentiable EML-tree symbolic regression (Odrzywolek 2026, arXiv:2603.21852). Streamlit UI + headless reproducer + full results.

A hands‑on, first‑principles guide to fitting logistic regression via the Iteratively Reweighted Least Squares (IRLS) algorithm complete with mathematical derivations, R code from scratch, and a real‑world S&P data case study to bring your statistical modeling skills to the next level.

A lightweight C++ tool that prices European call and put options using the Black–Scholes formula, computes all key Greeks (Δ, Γ, Θ, Vega, Rho), and lets you run quick ATM/ITM/OTM scenario checks—all via a simple command‑line interface.

A research-grade course in Numerical Methods and Optimization for ML. No deep learning libraries—just pure math and NumPy. Rebuilds automatic differentiation engines, stable SVD/Cholesky algorithms, quasi-Newton solvers (BFGS), stable log-sum-exp layers, and Gaussian Process regressions from scratch

This MATLAB function efficiently computes the inverse of a square matrix using LU factorization. By decomposing the matrix into lower and upper triangular matrices, the function solves for the inverse with improved numerical stability.

StableStockPredictor is a robust deep learning model for predicting S&P 500 stock prices, built with TensorFlow and Keras. It leverages LSTM networks with gradient clipping, robust scaling, and stable feature engineering (e.g., RSI, moving averages, volatility) to ensure reliable performance in volatile markets.

This course is part of the USC Graduate Biostatistics Program and is designed for second-year and beyond students interested in designing and implementing computational inferential tools for research.

Compute the arithmetic mean along one or more ndarray dimensions using a two-pass error correction algorithm.

17 specialized AI agents for comprehensive ML pipeline bug detection. Works with GitHub Copilot and Claude Code.

Bilimsel Hesaplama ve Sayısal Analiz masterclass'ı. Hazır kütüphaneler yok; saf matematik ve NumPy var. Makine epsilonu analizi, güvenli hibrit kök bulma, pivotlu LU/Cholesky çözücüler, Chebyshev interpolasyonu, uyarlamalı Gauss kuadratürü ve katı (stiff) diferansiyel denklemlerin (ODE/BVP) sıfırdan inşası.