Artur d'Avila Garcez
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Why Is Artur d'Avila Garcez Influential?
(Suggest an Edit or Addition)According to Wikipedia, Artur d'Avila Garcez is a researcher in the field of computational logic and neural computation, in particular hybrid systems with application in software verification and information extraction. His contributions include neural-symbolic learning systems and nonclassical models of computation combining robust learning and reasoning. He is a Professor of Computer Science at City, University London.
Artur d'Avila Garcez's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Neural-symbolic learning systems - foundations and applications (2012) (266)
- Symbolic knowledge extraction from trained neural networks: A sound approach (2001) (224)
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019) (188)
- Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge (2016) (182)
- Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) (179)
- The Connectionist Inductive Learning and Logic Programming System (1999) (163)
- Logic Tensor Networks for Semantic Image Interpretation (2017) (161)
- Neural-Symbolic Cognitive Reasoning (2008) (151)
- Neural-Symbolic Learning and Reasoning: Contributions and Challenges (2015) (146)
- Fast relational learning using bottom clause propositionalization with artificial neural networks (2013) (117)
- We Will Show Them! Essays in Honour of Dov Gabbay, Volume One (2005) (106)
- Neurosymbolic AI: The 3rd Wave (2020) (87)
- Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective (2020) (82)
- Measurable Counterfactual Local Explanations for Any Classifier (2019) (77)
- Speaker recognition with hybrid features from a deep belief network (2018) (74)
- Learning and Reasoning with Logic Tensor Networks (2016) (67)
- A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning (2011) (52)
- Logic Tensor Networks (2020) (50)
- Value-based Argumentation Frameworks as Neural-symbolic Learning Systems (2005) (48)
- Connectionist modal logic: Representing modalities in neural networks (2007) (47)
- Learning and Representing Temporal Knowledge in Recurrent Networks (2011) (45)
- A hybrid recurrent neural network for music transcription (2014) (39)
- Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach (2003) (39)
- Combining abductive reasoning and inductive learning to evolve requirements specifications (2003) (39)
- Logical Modes of Attack in Argumentation Networks (2009) (36)
- Fibring Neural Networks (2004) (35)
- An RNN-based Music Language Model for Improving Automatic Music Transcription (2014) (34)
- Reasoning about Time and Knowledge in Neural Symbolic Learning Systems (2003) (33)
- A Connectionist Cognitive Model for Temporal Synchronisation and Learning (2007) (27)
- Neural-Symbolic Learning Systems (2002) (25)
- A Connectionist Computational Model for Epistemic and Temporal Reasoning (2006) (25)
- Proceedings of 16th European Conference on Artificial Intelligence, ECAI 2004 (2004) (24)
- Abductive reasoning in neural-symbolic systems (2007) (23)
- An analysis-revision cycle to evolve requirements specifications (2001) (22)
- Knowledge Extraction from Deep Belief Networks for Images (2013) (22)
- Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017) (22)
- Computing First-Order Logic Programs by Fibring Artificial Neural Networks (2005) (21)
- Towards Symbolic Reinforcement Learning with Common Sense (2018) (20)
- Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples (2017) (18)
- Accuracy and Interpretability Trade-Offs in Machine Learning Applied to Safer Gambling (2016) (17)
- Applying connectionist modal logics to distributed knowledge representation problems (2004) (17)
- Connectionist computations of intuitionistic reasoning (2006) (17)
- Ontology learning as a use-case for neural-symbolic integration (2005) (16)
- A Distributed Model For Multiple-Viewpoint Melodic Prediction (2013) (15)
- Integrating model verification and self-adaptation (2010) (15)
- A neural cognitive model of argumentation with application to legal inference and decision making (2014) (15)
- Neural-Symbolic Intuitionistic Reasoning (2003) (14)
- Unimodal late fusion for NIST i-vector challenge on speaker detection (2014) (14)
- Reasoning About Requirements Evolution Using Clustered Belief Revision (2004) (12)
- Learning to adapt requirements specifications of evolving systems: (NIER track) (2011) (11)
- Generalising the Discriminative Restricted Boltzmann Machines (2017) (11)
- A Causal Loop Approach to the Study of Diagnostic Errors (2014) (10)
- Neural-Symbolic Integration for Fairness in AI (2021) (10)
- SOAR — Sparse Oracle-based Adaptive Rule extraction: Knowledge extraction from large-scale datasets to detect credit card fraud (2010) (10)
- Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach (2009) (10)
- Relational Knowledge Extraction from Neural Networks (2015) (10)
- Multi-instance learning using recurrent neural networks (2012) (10)
- Discriminative learning and inference in the Recurrent Temporal RBM for melody modelling (2015) (10)
- Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381) (2014) (9)
- Learning and reasoning about norms using neural-symbolic systems (2012) (9)
- Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study (2003) (8)
- The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks (2016) (8)
- Applying the connectionist inductive learning and logic programming system to power system diagnosis (1997) (8)
- Cognitive Algorithms and Systems: Reasoning and Knowledge Representation (2011) (8)
- First-order logic learning in Artificial Neural Networks (2010) (8)
- A Neural-Symbolic Cognitive Agent with a Mind's Eye (2012) (8)
- Learning in Informal Settings (2012) (8)
- Argumentation Neural Networks (2004) (7)
- Semi-supervised GANs for Fraud Detection* (2020) (7)
- Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition (2014) (7)
- Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO2 emissions (2014) (6)
- Inducing Relational Concepts with Neural Networks via the LINUS System (1998) (6)
- A Practical Tutorial on Explainable AI Techniques (2021) (6)
- Neural-symbolic networks for cognitive capacities (2014) (6)
- Neural-Symbolic Systems and the Case for Non-Classical Reasoning (2005) (5)
- Embedding Normative Reasoning into Neural Symbolic Systems (2011) (5)
- Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition (2015) (5)
- Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses (2015) (5)
- Feature Preprocessing with Restricted Boltzmann Machines for Music Similarity Learning (2014) (5)
- Advances in Neural-Symbolic Learning Systems: Modal and Temporal Reasoning (2007) (5)
- Making densenet interpretable a case study in clinical radiology (2019) (5)
- Extracting M of N Rules from Restricted Boltzmann Machines (2017) (5)
- Metalevel priorities and neural networks (2005) (5)
- Layerwise Knowledge Extraction from Deep Convolutional Networks (2020) (5)
- Dreaming Machines: On multimodal fusion and information retrieval using neural-symbolic cognitive agents (2013) (5)
- Learning and Development After School (2012) (5)
- Fat-Fast VG-RAM WNN: A high performance approach (2016) (4)
- Applied temporal Rule Mining to Time Series (2005) (4)
- Contrastive Counterfactual Visual Explanations With Overdetermination (2021) (4)
- Continual Learning Augmented Investment Decisions (2018) (4)
- Neural Networks for Runtime Verification (2014) (4)
- Towards the integration of abduction and induction in artificial neural networks (2006) (4)
- Sequence Classification Restricted Boltzmann Machines With Gated Units (2020) (4)
- Fewer Epistemological Challenges for Connectionism (2005) (4)
- Neural-Symbolic Reasoning Under Open-World and Closed-World Assumptions (2022) (4)
- Hybrid Long- and Short-Term Models of Folk Melodies (2015) (4)
- On the Relationship between I-O Logic and Connectionism (2010) (3)
- Reasoning and Learning About Past Temporal Knowledge in Connectionist Models (2007) (3)
- Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192) (2017) (3)
- A Neural Probabilistic Model for Predicting Melodic Sequences (2013) (3)
- Connectionist Non-classical Logics: Distributed Reasoning & Learning in Neural Networks (2008) (3)
- Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study (2010) (3)
- Journal of Applied Logic Special Volume on Neural-Symbolic Systems (2004) (3)
- An integrated neural-symbolic cognitive agent architecture for training and assessment in simulators (2010) (3)
- Improving VG-RAM Neural Networks Performance Using Knowledge Correlation (2006) (3)
- Geometric Semi-automatic Analysis of Colles’ Fractures (2020) (3)
- Adaptive Transferred-profile Likelihood Learning (2016) (3)
- A I ] 1 0 D ec 2 02 0 Neurosymbolic AI : The 3 rd Wave (2020) (3)
- On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision (2020) (3)
- Feature Preprocessing with RBMs for Music Similarity Learning (2013) (3)
- A Comparison between Deep Q-Networks and Deep Symbolic Reinforcement Learning (2017) (3)
- Probabilistic approaches for music similarity using restricted Boltzmann machines (2019) (2)
- Using inductive types for ensuring correctness of neuro-symbolic computations (2010) (2)
- Generalising the Discriminative Restricted Boltzmann Machine (2016) (2)
- Learning Through the Breach: Language Socialization (2012) (2)
- Neural symbolic architecture for normative agents (2011) (2)
- Neural-Symbolic Integration: The Road Ahead (2002) (2)
- Learning from Text (2012) (2)
- Neural-symbolic cognitive agents: architecture, theory and application (2014) (2)
- Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction (2013) (2)
- Synthetic Data Generation for Fraud Detection using GANs (2021) (2)
- Multiple Viewpiont Melodic Prediction with Fixed-Context Neural Networks (2014) (2)
- Runtime Verification Through Forward Chaining (2014) (2)
- A Connectionist Model for Constructive Modal Reasoning (2005) (2)
- Learning to Act with RVRL Agents (2007) (2)
- Knowledge Extraction from Trained Networks (2002) (1)
- Making Good on LSTMs Unfulfilled Promise (2019) (1)
- Convolutional Data : Towards Deep Audio Learning from Big Data ( Abstract ) (2014) (1)
- Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples (2017) (1)
- Adaptive Feature Ranking for Unsupervised Transfer Learning (2013) (1)
- A neural-symbolic perspective on analogy (2008) (1)
- A Practical Guide on Explainable Ai Techniques Applied on Biomedical Use Case Applications (2021) (1)
- Neural-symbolic monitoring and adaptation (2015) (1)
- Towards a Connectionist Argumentation Framework (2004) (1)
- A machine learning approach for Colles’ fracture treatment diagnosis (2020) (1)
- Reports of the AAAI 2010 Conference Workshops (2010) (1)
- On Gabbay ’ s Fibring Methodology for Bayesian and Neural Networks (1)
- Editorial: Booming of Neural Networks and Learning Systems (2019) (1)
- Accountability in AI: From Principles to Industry-specific Accreditation (2021) (1)
- Inductive Learning in Shared Neural Multi-Spaces (2017) (1)
- A Proposal for Common Dataset in Neural-Symbolic Reasoning Studies (2016) (1)
- Relational Knowledge Extraction from Attribute-Value Learners (2013) (1)
- Extended theory refinement in knowledge-based neural networks (2002) (1)
- Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches - Volume 1583 (2015) (1)
- Neuro-symbolic Representation of Logic Programs Defining Infinite Sets (2010) (1)
- Combining Architectures for Temporal Learning in Neural-Symbolic Systems (2006) (1)
- Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding (2021) (1)
- Logical Boltzmann Machines (2021) (1)
- Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases (2020) (1)
- Counterfactual Instances Explain Little (2021) (1)
- Neuro-Symbolic Probabilistic Argumentation Machines (2020) (1)
- Extracting Meaningful High-Fidelity Knowledge from Convolutional Neural Networks (2022) (1)
- Reports of the AAAI 2012 Conference Workshops (2012) (1)
- Towards Reasoning about the Past in Neural-symbolic Systems (2007) (1)
- Proceedings of the IJCAI-05 Workshop on Neural-Symbolic Learning and Reasoning, NeSy'05, Edinburgh, UK, 1st of August 2005 (2005) (1)
- Experiments on Handling Inconsistencies (2002) (0)
- Formalizing Consistency and Coherence of Representation Learning (2022) (0)
- Low-Cost Representation for Restricted Boltzmann Machines (2014) (0)
- Proceedings of 19th National Conference on Artificial Itnelligence (2004) (0)
- Probabilistic approaches for music similarity using restricted Boltzmann machines (2019) (0)
- Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction (2018) (0)
- Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper (2022) (0)
- Experiments on Theory Refinement (2002) (0)
- Efficient predicate invention using shared "NeMuS" (2019) (0)
- Category-based Inductive Learning in Shared NeMuS (2017) (0)
- Scalable Process Monitoring through Rules and Neural Networks (2014) (0)
- Rule Extraction from Support Vector Machines: A Geometric Approach. Technical Report (2008) (0)
- Report from Dagstuhl Seminar 17192 Human-Like Neural-Symbolic Computing (2017) (0)
- A pr 2 01 6 Generalising the Discriminative Restricted Boltzmann Machine (0)
- The Recurrent Temporal Discriminative Restricted Boltzmann Machines (2017) (0)
- On the memory properties of recurrent neural models (2017) (0)
- Confidence Values and Compact Rule Extraction From Probabilistic Neural Networks (2017) (0)
- Editorial (2007) (0)
- Proceedings of the 3rd International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'07, held at IJCAI-07, Hyderabad, India, January 8, 2007 (2007) (0)
- Theory Refinement in Neural Networks (2002) (0)
- Neural-Symbolic Cognitive Agents: Architecture and Theory (2011) (0)
- Neural-Symbolic Rule-Based Monitoring (2012) (0)
- Learning and extracting tacit knowledge from processes using the Neural- Symbolic paradigm (2015) (0)
- Modelling Clinical Diagnostic Errors: A System Dynamics Approach (2015) (0)
- Preface: Reinforcement Learning (2009) (0)
- Linear-Time Sequence Classification using Restricted Boltzmann Machines (2017) (0)
- Experiments on Knowledge Extraction (2002) (0)
- Proceedings of the 3rd International Conference on Neural-Symbolic Learning and Reasoning - Volume 230 (2007) (0)
- Editorial (2009) (0)
- Efficient representation ranking for transfer learning (2015) (0)
- Proceedings of ACM ESEC/FSE International Workshop on Intelligent Technologies for Software Engineering WITSE03 (2003) (0)
- A System Dynamics Approach to Analyze Laboratory Test Errors (2015) (0)
- Learning about Actions and Events in Shared NeMuS (2017) (0)
- Neurons and Symbols: A Manifesto (2010) (0)
- A Semantic Framework for Neural-Symbolic Computing (2022) (0)
- Fast Relational Learning using Bottom Clauses in Neural Networks (2012) (0)
- Coherent and Consistent Relational Transfer Learning with Auto-encoders (2021) (0)
- Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning (2023) (0)
- Handling Inconsistencies in Neural Networks (2002) (0)
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