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08/23/2023
Relational Concept Based Models
The design of interpretable deep learning models working in relational d...
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04/27/2023
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision pro...
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03/23/2023
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge
Knowledge Graph Embeddings (KGE) have become a quite popular class of mo...
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03/08/2023
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic bac...
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03/06/2023
Safe Reinforcement Learning via Probabilistic Logic Shields
Safe Reinforcement learning (Safe RL) aims at learning optimal policies ...
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09/19/2022
Concept Embedding Models
Deploying AI-powered systems requires trustworthy models supporting effe...
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02/07/2022
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
We present VAEL, a neuro-symbolic generative model integrating variation...
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08/25/2021
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
Neural-symbolic and statistical relational artificial intelligence both ...
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06/23/2021
DeepStochLog: Neural Stochastic Logic Programming
Recent advances in neural symbolic learning, such as DeepProbLog, extend...
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06/01/2021
Learning Representations for Sub-Symbolic Reasoning
Neuro-symbolic methods integrate neural architectures, knowledge represe...
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09/26/2020
Online Learning of Non-Markovian Reward Models
There are situations in which an agent should receive rewards only after...
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05/05/2020
Deep Lagrangian Constraint-based Propagation in Graph Neural Networks
Several real-world applications are characterized by data that exhibit a...
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03/18/2020
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both i...
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02/18/2020
Local Propagation in Constraint-based Neural Network
In this paper we study a constraint-based representation of neural netwo...
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02/18/2020
A Lagrangian Approach to Information Propagation in Graph Neural Networks
In many real world applications, data are characterized by a complex str...
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02/06/2020
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several ta...
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09/06/2019
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances
We consider a scenario where an artificial agent is reading a stream of ...
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07/26/2019
Learning and T-Norms Theory
Deep learning has been shown to achieve impressive results in several do...
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07/19/2019
An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning
In the last few years, neural networks have been intensively used to dev...
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07/18/2019
On the relation between Loss Functions and T-Norms
Deep learning has been shown to achieve impressive results in several do...
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05/31/2019
Neural Markov Logic Networks
We introduce Neural Markov Logic Networks (NMLNs), a statistical relatio...
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03/18/2019
LYRICS: a General Interface Layer to Integrate AI and Deep Learning
In spite of the amazing results obtained by deep learning in many applic...
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01/14/2019
Integrating Learning and Reasoning with Deep Logic Models
Deep learning is very effective at jointly learning feature representati...
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08/21/2018
Backpropagation and Biological Plausibility
By and large, Backpropagation (BP) is regarded as one of the most import...
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07/17/2018
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
The effectiveness of deep neural architectures has been widely supported...
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07/16/2018