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Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)
A 2D Gaussian Splatting paper for no obvious reasons. Enjoy!
A novel architectural design for stitching video streams in real-time on an FPGA.
Evaluation of few methods to apply Gaussian Blur on an Image.
Fast Incremental Support Vector Data Description implemented in Python
An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection
Statistical pattern recognition course projects from shiraz university.
Implementation of Kernel-Density-Estimation (KDE) with Matlab
Official implementation of PromptSplit: kernel-based framework to detect prompt-level disagreements in generative models (text-to-image, LLMs). Identifies divergent prompt clusters via tensor embeddings & random projections for scalability.
Primal-Dual algorithm for smooth regularization of non-smooth optimization functions
Signal and Systems course project - Fall 2021 - voice activity detection using gaussian kernel and adaptive threshold
We use support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help us gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, we use support vector machines to build a spam classifier.
PyTorch implementation of important functions for WAIL and GMMIL
Classification of a radially seperated dataset using SVM with RBF kernel using CVXOPT
Fraud detection over twitter feed data
Real-time crowd density estimation using CSRNet (VGG-16 + dilated CNN) on ShanghaiTech Part A & B. Includes adaptive Gaussian kernel density map generation, from-scratch training on M1 CPU, MPS inference, and a live video demo with heatmap overlay and running count graph. Part A MAE: 87.85 | Part B MAE: 21.12
Classification of wine quality using a hard_parzen and a soft_parzen with gaussian kernel models - Machine Learning course (IFT3395)
Classification on the Web Spam Dataset using Percepton and Kernel Perceptron with Polynomial, Gaussian, Exponential and Laplacian Kernels.
classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching
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