Search Results for author:
Found 27 papers, 8 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.
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.
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.
Safe Reinforcement Learning via Probabilistic Logic Shields
To this end, we introduce Probabilistic Logic Policy Gradient (PLPG).
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.
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
DeepStochLog: Neural Stochastic Logic Programming
Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.
Relational Reasoning Networks
However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications.
Online Learning of Non-Markovian Reward Models
In our formal setting, we consider a Markov decision process (MDP) that models the dynamics of the environment in which the agent evolves and a Mealy machine synchronized with this MDP to formalize the non-Markovian reward function.
Deep Constraint-based Propagation in Graph Neural Networks
The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs).
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.
Local Propagation in Constraint-based Neural Network
In this paper we study a constraint-based representation of neural network architectures.
A Lagrangian Approach to Information Propagation in Graph Neural Networks
GNNs exploit a set of state variables, each assigned to a graph node, and a diffusion mechanism of the states among neighbor nodes, to implement an iterative procedure to compute the fixed point of the (learnable) state transition function.
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available.
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 text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read.
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.
An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning
In the last few years, neural networks have been intensively used to develop meaningful distributed representations of words and contexts around them.
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.
Neural Markov Logic Networks
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.
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.
Backpropagation and Biological Plausibility
This might open the doors to a truly novel class of learning algorithms where, because of the introduction of the notion of support neurons, the optimization scheme also plays a fundamental role in the construction of the architecture.
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles.
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.
