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Found 27 papers, 5 papers with code
Modular Neural Networks for Time Series Forecasting: Interpretability and Feature Selection using Attention
A modular deep network is trained from the selected features independently to show the users how features influence outcomes, making the model interpretable.
Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
The highest classification accuracy 0. 854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network. Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms.
Proceedings 39th International Conference on Logic Programming
This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023.
A Semantic Framework for Neural-Symbolic Computing
The field of neural-symbolic AI attempts to exploit this asymmetry by combining neural networks and symbolic AI into integrated systems.
Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding
We propose neural-symbolic integration for abstract concept explanation and interactive learning.
Logical Boltzmann Machines
The idea of representing symbolic knowledge in connectionist systems has been a long-standing endeavour which has attracted much attention recently with the objective of combining machine learning and scalable sound reasoning.
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Synthetic Data Generation for Fraud Detection using GANs
Detecting money laundering in gambling is becoming increasingly challenging for the gambling industry as consumers migrate to online channels.
Counterfactual Instances Explain Little
We will further illustrate how explainable AI methods that provide both causal equations and counterfactual instances can successfully explain machine learning predictions.
Contrastive Counterfactual Visual Explanations With Overdetermination
A novel explainable AI method called CLEAR Image is introduced in this paper.
Logic Tensor Networks
In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning.
Neurosymbolic AI: The 3rd Wave
Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry.
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision
We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties.
Layerwise Knowledge Extraction from Deep Convolutional Networks
We apply this method to a variety of deep networks and find that in the internal layers we often cannot find rules with a satisfactory complexity and accuracy, suggesting that rule extraction as a general purpose method for explaining the internal logic of a neural network may be impossible.
Making Good on LSTMs' Unfulfilled Promise
We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i. e. which memory did what and when.
Measurable Counterfactual Local Explanations for Any Classifier
We propose a novel method for explaining the predictions of any classifier.
Efficient predicate invention using shared "NeMuS"
Amao is a cognitive agent framework that tackles the invention of predicates with a different strategy as compared to recent advances in Inductive Logic Programming (ILP) approaches like Meta-Intepretive Learning (MIL) technique.
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems.
Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
In this paper we examine this question systematically by proposing a knowledge extraction method using \textit{M-of-N} rules which allows us to map the complexity/accuracy landscape of rules describing hidden features in a Convolutional Neural Network (CNN).
Continual Learning Augmented Investment Decisions
Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making.
Towards Symbolic Reinforcement Learning with Common Sense
Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
Logic Tensor Networks for Semantic Image Interpretation
Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data.
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.
Generalising the Discriminative Restricted Boltzmann Machine
Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice.
Adaptive Feature Ranking for Unsupervised Transfer Learning
Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain.
