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)

Do models distinguish between declared-true and declared-false premises?

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

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.

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

🧠 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.

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.

Intelligent layer pruning toolkit for LLMs featuring iterative optimization, self-healing algorithms, and comprehensive benchmarking.

Ablation Study of CapsuleNetwork on TimeSeries

🛠️ Optimize LLMs with advanced pruning strategies and real-time visualization for smaller, faster models without losing intelligence.

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

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

CNN implementation for CIFAR-10 image classification with hyperparameter ablation study.

Optimize ResNet Learning Process

Comparative study of ANN, CNN, and Hybrid architectures for CIFAR-10 image classification, achieving 91%+ accuracy with custom-built models and comprehensive ablation studies.

In-depth ablation study of the DetectGPT paper

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