A comprehensive look into the Importance Sampling and Path Guiding for Path Tracing
Exploring the Importance Sampling Techniques to reduce variance in Monte Carlo Path Tracing
Posts
3D Reconstruction with 3DGS, NeRF, and Their Variants
An in-depth look at modern 3D reconstruction methods such as 3DGS and NeRF, comparing their architectures, strengths, and performance in diverse applications.
Bagging and Boosting
Explore bagging and boosting in machine learning, including variance reduction, bias management, and ensemble classifier construction
Bias–Variance Tradeoff
Understanding how bias and variance affect model performance
Vision Transformer (ViT) - Architecture and Implementation
Understanding how transformers use scaled dot-product attention with multiple heads to process sequential data efficiently
Linear Regression with MLE and MAP
Linear Regression using MLE and MAP with visualization and implementation
Parameter Estimation: From MLE to Full Bayesian Inference
Maximum Likelihood, MAP and Bayesian Inference for probability estimation
Dimensionality Reduction using PCA
Notes on the minimum reconstruction error interpretation of PCA and high dimensional PCA