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Found 18 papers, 7 papers with code
Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models
"), and imagine alternative scenarios that could result in different predictions (the "What if?").
Relational Concept Based Models
The design of interpretable deep learning models working in relational domains poses an open challenge: interpretable deep learning methods, such as Concept-Based Models (CBMs), are not designed to solve relational problems, while relational models are not as interpretable as CBMs.
Categorical Foundations of Explainable AI: A Unifying Theory
Explainable AI (XAI) aims to address the human need for safe and reliable AI systems.
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust.
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge
In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset.
Extending Logic Explained Networks to Text Classification
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy.
Logic Explained Networks
The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience.
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.
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Relational Reasoning Networks
However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications.
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available.
Conditions for Unnecessary Logical Constraints in Kernel Machines
A main property of support vector machines consists in the fact that only a small portion of the training data is significant to determine the maximum margin separating hyperplane in the feature space, the so called support vectors.
T-Norms Driven Loss Functions for Machine Learning
Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data.
On the relation between Loss Functions and T-Norms
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing.
LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning
In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference.
Integrating Learning and Reasoning with Deep Logic Models
Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns.
Constraint-Based Visual Generation
We use deep architectures to model the involved variables, and propose a computational scheme where the learning process carries out a satisfaction of the constraints.
Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow
This report provides an introduction to some Machine Learning tools within the most common development environments.
