Add to ORKGLinear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exist solvers for even high-dimensional LPs, understanding said high-dimensional solutions poses an orthogonal and unresolved problem. We introduce an approach where we consider neural encodings for LPs that justify the application of attribution methods from explainable artificial intelligence (XAI) designed for neural learning systems. The several encoding functions we propose take into account aspects such as feasibility of the decision space, the cost attached to each input, or the distance to special points of interest. We investigate the mathematical consequenc...
The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and...
The recent years have been marked by extended research on adversarial attacks, especially on deep ne...
Attribution methods have been developed to understand the decision making process of machine learnin...
There has been a recent push in making machine learning models more interpretable so that their perf...
As deep learning (DL) efficacy grows, concerns for poor model explainability grow also. Attribution ...
The last decade has witnessed an increasing adoption of black-box machine learning models in a varie...
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their bl...
Model attributions are important in deep neural networks as they aid practitioners in understanding...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
A fundamental bottleneck in utilising complex machine learning systems for critical applications has...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
With the rise of deep neural networks, the challenge of explaining the predictions of these networks...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
Different users of machine learning methods require different explanations, depending on their goals...
The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and...
The recent years have been marked by extended research on adversarial attacks, especially on deep ne...
Attribution methods have been developed to understand the decision making process of machine learnin...
There has been a recent push in making machine learning models more interpretable so that their perf...
As deep learning (DL) efficacy grows, concerns for poor model explainability grow also. Attribution ...
The last decade has witnessed an increasing adoption of black-box machine learning models in a varie...
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their bl...
Model attributions are important in deep neural networks as they aid practitioners in understanding...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
A fundamental bottleneck in utilising complex machine learning systems for critical applications has...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
With the rise of deep neural networks, the challenge of explaining the predictions of these networks...
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that h...
Different users of machine learning methods require different explanations, depending on their goals...
The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and...
The recent years have been marked by extended research on adversarial attacks, especially on deep ne...
Attribution methods have been developed to understand the decision making process of machine learnin...