ablation

Hypersonic / Rarefied gas dynamics code developments (GPL-3.0)

Model Ablation Tool-Kit for Deep Learning Model

Distribution transparent Machine Learning experiments on Apache Spark

The production engine for directional ablation. Unalign / remove models censorship efficiently on any hardware.

Extract and evaluate radiomics for liver cancer tumors from DICOM segmentation masks. Using SimpleITK, PyRadiomics and PyDicom.

Explainability of Deep Learning Models

Evaluating XAI methods through ablation studies.

Explainability of Brain Tumour Segmentation Models

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

Library to compute 3D surface-distances for evaluating liver ablation/tumor completeness based on segmentation images.

Dataset for ablative TPS numerical test cases from the ESA AblaNTIS project.

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

This is a tool for modeling river bank erosion in permafrost environments

Targeted semantic multimodal input ablation. Official implementation of the ablation method introduced in the paper: "What Vision-Language Models 'See' when they See Scenes"

Working with Chest X-Ray (CXR) images : Medical Imaging

Does a CLAUDE.md actually change how Claude behaves? An ablation harness: run adversarial traps with the rules and without them, grade blind, and test whether the difference is real.

Theoretical review of physics-guided multimodal strategies for WHO Grade IV tumours, covering imaging, radiotherapy, ablation, hyperthermia, TTFields, safety limits, ethics, and future oncology research.

This final project uses two machine learning models, XGBoost and CatBoost, to train and predict outcomes on the given dataset.

Causal Chain-of-Thought step faithfulness evaluation harness — quantifies whether reasoning steps actually drive model outputs or are decorative