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

Distribution transparent Machine Learning experiments on Apache Spark

This project implements 30+ variants of ANN algorithms to find the K nearest neighbors in high-dimensional vector spaces. It is meant as a convenient sandbox: drop in your own ANN code, run a one-liner, and instantly compare build/search speed and recall against the bundled baselines.

Testing whether models distinguish declared-true from declared-false premises

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

A light-weight library for fast-ablation studies on GPT-like Language Models.

O(N) attention with a bounded inference KV cache. D4 Daubechies wavelet field + content-gated Q·K gather at dyadic offsets.

Beautiful modular D3QN research pipeline with training, ablations, plots, report, and packaging

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.

This project investigates the robustness of humanoid locomotion policies trained with imitation learning and reinforcement learning in simulation. The primary research question is: how does a learned PPO controller respond to partial actuator or degree-of-freedom failure, and which joints are most critical for maintaining stable locomotion?

🧠 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.

Emotiwave is a research project investigating how well AI systems can recognise human emotions from video when one or more sensors fail. The core question: if you lose the audio, or the camera, or the transcript — does the system fall apart, or does it adapt?

Six Ways to Forget: Biologically-grounded forgetting mechanisms for LLM agent memory systems. 18 experiments, 4 falsified hypotheses, STDP ablation (Cohen's d = 3.163).

A comprehensive experimental lab for Looped Transformers from scratch. Includes an automated ablation pipeline (ExperimentTable) and a real-time monitoring dashboard.

Phase zero of Artificial Neuroplasticity: Giving models self-editing capacity, through a trained triumvirate of three models; Analyzer / Trainee / Evaluator. The Analyzer uses TransformerLens to watch the Trainee. The Evaluator is the Review Board,, confirming the Trainee has become smarter than itself. This IS NOT implemented in this phase zero.

Multi-agent verification for AI outputs: claim verification, RAG diagnostics, pre-action verification for agentic AI. Includes ablation studies proving multi-agent vs single-prompt tradeoffs, FaithBench benchmarks, and bias-triggering evaluation methodology

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

SAGE: Self-Adaptive Goal-directed Executor — A multi-tool LLM agent with DAG-based hierarchical planning, ReAct reasoning, and evidence-guided self-correction for automated research synthesis. Built from scratch, no frameworks.

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