Toolkit for fine-tuning, ablating and unit-testing open-source LLMs.

Distribution transparent Machine Learning experiments on Apache Spark

Attentively Embracing Noise for Robust Latent Representation in BERT (COLING 2020)

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

This study tries to compare the detection of lung diseases using xray scans from three different datasets using three different neural network architectures using Pytorch and perform an ablation study by changing learning rates. The dimensional understanding is visualised using t-SNE and Grad-CAM for visualisation of diseases in x-ray scans.

Re-implementation of the paper titled "Noise against noise: stochastic label noise helps combat inherent label noise" from ICLR 2021.

Ablation Study of CapsuleNetwork on TimeSeries

Course project for AFLT class, Spring 2022: Exploring SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines

Design of an ablation study for a Machine Learning pipeline. The effect of preprocessing, model or postprocessing modules can be automatically tested.

Classified human and machine generated text using 1) a single score threshold classifier and 2) a neural network classifier approach, based on perplexities and probability scores generated from n-grams. Best results are 77% for the single score classifier and 80% for the ANN classifier.

Optimize ResNet Learning Process

Research project aimed at developing a prediction model to estimate the number of upvotes of a given Reddit post.

KNN Classification on Abalone dataset and Ablation study on normalization of the data