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Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both...
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Making Good on LSTMs Unfulfilled Promise
LSTMs promise much to financial time-series analysis, temporal and cross...
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Continuous Learning Augmented Investment Decisions
Investment decisions can benefit from incorporating an accumulated knowl...
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Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
We introduce a method for transferring style from the logos of heavy met...
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Layerwise Knowledge Extraction from Deep Convolutional Networks
Knowledge extraction is used to convert neural networks into symbolic de...
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Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
Current advances in Artificial Intelligence and machine learning in gene...
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Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited b...
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Measurable Counterfactual Local Explanations for Any Classifier
We propose a novel method for explaining the predictions of any classifi...
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The Recurrent Temporal Discriminative Restricted Boltzmann Machines
The recurrent temporal restricted Boltzmann machine (RTRBM) has been suc...
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Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
We propose Logic Tensor Networks: a uniform framework for integrating au...
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Logic Tensor Networks for Semantic Image Interpretation
Semantic Image Interpretation (SII) is the task of extracting structured...
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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 ...
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Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clini...
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Well-Founded Argumentation Semantics for Extended Logic Programming
This paper defines an argumentation semantics for extended logic program...
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Revisited Experimental Comparison of Node-Link and Matrix Representations
Visualizing network data is applicable in domains such as biology, engin...
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The Emergence of Consensus: A Primer
The origin of population-scale coordination has puzzled philosophers and...
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Mitigating CSRF attacks on OAuth 2.0 and OpenID Connect
Many millions of users routinely use their Google, Facebook and Microsof...
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Towards Symbolic Reinforcement Learning with Common Sense
Deep Reinforcement Learning (deep RL) has made several breakthroughs in ...
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Undecidability of MSO+"ultimately periodic"
We prove that MSO on ω-words becomes undecidable if allowing to quantify...
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M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
In this paper, we present a novel neural network architecture for retina...
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Improved Speech Enhancement with the Wave-U-Net
We study the use of the Wave-U-Net architecture for speech enhancement, ...
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Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations
Basic binary relations such as equality and inequality are fundamental t...
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Modelling Identity Rules with Neural Networks
In this paper, we show that standard feed-forward and recurrent neural n...
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Continual Learning Augmented Investment Decisions
Investment decisions can benefit from incorporating an accumulated knowl...
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Progressive Data Science: Potential and Challenges
Data science requires time-consuming iterative manual activities. In par...
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OAuthGuard: Protecting User Security and Privacy with OAuth 2.0 and OpenID Connect
Millions of users routinely use Google to log in to websites supporting ...
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Geometric Multicut
We study the following separation problem: Given a collection of colored...
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Constrained Mutual Convex Cone Method for Image Set Based Recognition
In this paper, we propose a method for image-set classification based on...
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Continuous chain-ladder with paid data
We introduce a model where iid payments generate the traditional paid ru...
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Non-Smooth Backfitting for Excess Risk Additive Regression Model with Two Survival Time-Scales
We present a new backfitting algorithm estimating the complex structured...
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Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data
Estimating spot covariance is an important issue to study, especially wi...
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Factors for the Generalisation of Identity Relations by Neural Networks
Many researchers implicitly assume that neural networks learn relations ...
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Efficient predicate invention using shared "NeMuS"
Amao is a cognitive agent framework that tackles the invention of predic...
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An Ontology-based Approach to Explaining Artificial Neural Networks
Explainability in Artificial Intelligence has been revived as a topic of...
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A Framework for Multiaccess Support for Unreliable Internet Traffic using Multipath DCCP
Mobile nodes are typically equipped with multiple radios and can connect...
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Criteria for Rigor in Visualization Design Study
We develop a new perspective on research conducted through visualization...
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Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations
While previous work exists on how to conduct and disseminate insights fr...
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Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
There is an urgent societal need to assess whether autonomous vehicles (...
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Words of Estimative Correlation: Studying Verbalizations of Scatterplots
Multimodal approaches where interactive visualization and natural langua...
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Weight Priors for Learning Identity Relations
Learning abstract and systematic relations has been an open issue in neu...
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Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals
Robustness against temporal variations is important for emotion recognit...
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Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontol...
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Detection of Pitt-Hopkins Syndrome based on morphological facial features
This work describes an automatic methodology to discriminate between ind...
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The Strahler number of a parity game
The Strahler number of a rooted tree is the largest height of a perfect ...
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Scalar Product Lattice Computation for Efficient Privacy-preserving Systems
Privacy-preserving applications allow users to perform on-line daily act...
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Blissful Ignorance: Anti-Transfer Learning for Task Invariance
We introduce the novel concept of anti-transfer learning for neural netw...
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The COVID-19 online shadow economy
The COVID-19 pandemic has reshaped the demand for goods and services wor...
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Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
In this paper, a deep reinforcement learning (DRL) method is proposed to...
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