Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
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Updated
Jun 3, 2024 - Python
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
Getting explanations for predictions made by black box models.
Program that helps optimize our algorithm
Codes for AAAI 2024 paper: LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate
Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence
Mitigating the high computational costs associated with applying Bayesian model updating in inverse problems / Uncertainty Quantification and Efficient Sensitivity Analysis by using Surrogate Models
Package for data-driven and phenomenological gravitational waveform models
Bypassing slow numerical simulators of gravitational wave physics with machine learning.
Implementation of a new pointwise metric using Keras and Abaqus.
Neural architecture search for object detectors using non dominated sorting genetic algorithm and surrogate optimization
Source code of "On the influence of over-parameterization in manifold based surrogates and deep neural operators".
Surrogarte modelling technique selector
Interpreting Categorical Data Classifiers using Explanation-based Locality
Pytorch based reimplementation of COMS: Conservative Objective Models for Effective Offline Model-Based Optimization.
This repository consist of a compendium of assignments and their respective solutions for an advanced course in Applied Bayesian Statistics
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Machine Learning as an alternative to simulation models for decision making
Two Blade Propeller Surrogate Model using XGBoost Algorithm
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