LLMRepos·Classical ML LLM projects

Research on Tabular Deep Learning: Papers & Packages

(ICLR 2025) TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling

[TPAMI 2023] LibFewShot: A Comprehensive Library for Few-shot Learning.

Fast & Simple Resource-Constrained Learning of Deep Network Structure

Production-ready K-Means clustering for Apache Spark with pluggable Bregman divergences (KL, Itakura-Saito, L1, etc). 6 algorithms, 740 tests, cross-version persistence. Drop-in replacement for MLlib with mathematically correct distance functions for probability distributions, spectral data, and count data.

Deep neural network kernel for Gaussian process

Plant Disease Detection is one of the mind-boggling issues when we talk about using Technology in Agriculture. Although researches have been done to detect whether a plant is healthy or diseased using Deep Learning and with the help of Neural Network, new techniques are still being discovered. For Fewer Data Classical Machine Learning Models are said to outstand given the data is pre-processed well. On the same theory here is my approach for Detecting whether a plant leaf is healthy or unhealthy by utilizing the classical Machine Learning Models, Pre-processing the Image Data. The data was fed to 7 Machine Learning Models with 10 fold cross-validation out of which Random Forest Classifier outperformed all the other models giving an accuracy of 97% on the test set.