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Found 13 papers, 2 papers with code
Multitask Kernel-based Learning with Logic Constraints
This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines.
Multitask Kernel-based Learning with First-Order Logic Constraints
In this paper we propose a general framework to integrate supervised and unsupervised examples with background knowledge expressed by a collection of first-order logic clauses into kernel machines.
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.
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.
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.
Relational Reasoning Networks
However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications.
Contrastive Losses and Solution Caching for Predict-and-Optimize
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available.
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.
