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

🧠 Automated neural network ablation studies using LLM agents and LangGraph. Systematically remove components, test performance, and gain insights into architecture importance through an intelligent multi-agent workflow.

A multimodal deep learning project for classifying mental health-related memes, combining both textual and visual features.

Reproducible research comparing GNN (GraphSAGE, GCN, GAT) vs ML baselines (XGBoost, RF) on Elliptic++ Bitcoin fraud detection. Features ablation experiments revealing when tabular models outperform graph neural networks.

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

Optimize ResNet Learning Process

Generative Chatbot using Sequence-to-sequence Deep Learning (including 13 models) for ablation study

In-depth ablation study of the DetectGPT paper

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

Lightweight CNN for 28×28 grayscale multi-class image classification with augmentation & regularization.

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

A Comparative Analysis of BART and PEGASUS for Abstractive News Summarization

End-to-End Python replication of Iadisernia & Camassa’s LLM macroeconomic forecasting methodology (ICAIF 2025). Implements: 2,368 synthetic economist profiles, 120,000+ GPT-4o forecasts across 50 European Central Bank (ECB) SPF rounds, a rigorous ablation study with Monte Carlo & binomial hypothesis testing.

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