Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda | Paper Citation Record

Observation d5dce593-7649-4bc7-af85-96d6d9268d94 · outbound

This paper cites Deep learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Deep learning,

Reference 1

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This paper cites A survey on deep learning and its applications,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda A survey on deep learning and its applications,

Reference 2

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This paper cites Deep learning is hitting a wall,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Deep learning is hitting a wall,

Reference 3

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

Reference 4

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This paper cites Causality: The next step in artificial intelligence,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Causality: The next step in artificial intelligence,

Reference 5

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This paper cites Causal Deep Learning.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Causal Deep Learning

Reference 6

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This paper cites When causal inference meets deep learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda When causal inference meets deep learning,

Reference 7

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This paper cites Do humans think causally, and how?.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Do humans think causally, and how?

Reference 8

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This paper cites When noise meets chaos: Stochastic resonance in neurochaos learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda When noise meets chaos: Stochastic resonance in neurochaos learning,

Reference 9

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This paper cites Neurochaos feature transformation for machine learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Neurochaos feature transformation for machine learning,

Reference 10

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This paper cites Causality preserving chaotic transformation and classification using neurochaos learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Causality preserving chaotic transformation and classification using neurochaos learning,

Reference 11

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This paper cites Linked data: Principles and state of the art,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Linked data: Principles and state of the art,

Reference 12

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This paper cites Opportunities for neuromorphic computing algorithms and applications,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Opportunities for neuromorphic computing algorithms and applications,

Reference 13

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This paper cites Pearl and D.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Pearl and D

Reference 14

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This paper cites Pearl, M.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Pearl, M

Reference 15

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This paper cites An introduction to causal inference,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda An introduction to causal inference,

Reference 16

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This paper cites A tutorial on learning with bayesian networks,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda A tutorial on learning with bayesian networks,

Reference 17

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This paper cites Causalkg: Causal knowledge graph explain- ability using interventional and counterfactual reasoning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Causalkg: Causal knowledge graph explain- ability using interventional and counterfactual reasoning,

Reference 18

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This paper cites Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain,

Reference 19

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This paper cites Stochastic resonance in climatic change,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Stochastic resonance in climatic change,

Reference 20

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This paper cites Neurochaos inspired hybrid machine learning architecture for classification,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Neurochaos inspired hybrid machine learning architecture for classification,

Reference 21

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This paper cites Chaosnet: A chaos based artificial neural network architecture for classification,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Chaosnet: A chaos based artificial neural network architecture for classification,

Reference 22

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This paper cites What is a support vector machine?.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda What is a support vector machine?

Reference 23

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This paper cites Relating Graph Neural Networks to Structural Causal Models.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Relating Graph Neural Networks to Structural Causal Models

Reference 24

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This paper cites Link prediction based on graph neural net- works,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Link prediction based on graph neural net- works,

Reference 25

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This paper cites Graph neu- ral networks and reinforcement learning: A survey,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Graph neu- ral networks and reinforcement learning: A survey,

Reference 26

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

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This paper cites The semantic web,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda The semantic web,

Reference 28

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This paper cites Exploring Causal Learning through Graph Neural Networks: An In-depth Review.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Exploring Causal Learning through Graph Neural Networks: An In-depth Review

Reference 29

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Estimation of the kullback-leibler divergence,

Reference 30

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

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This paper cites Causal graphsage: A robust graph method for classification based on causal sampling,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Causal graphsage: A robust graph method for classification based on causal sampling,

Reference 34

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Inductive representation learning on large graphs,

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Logistic map: A possible random-number generator,

Reference 36

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Robust neural networks using stochastic resonance neurons,

Reference 37

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Neural spiking for causal inference and learning,

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Signn: A spike- induced graph neural network for dynamic graph representation learn- ing,

Reference 39

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Unresolved cited work

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Stochastic graph as a model for social networks,

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Graph Neural Stochastic Differential Equations

Reference 42

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This paper cites A brain-inspired causal reasoning model based on spiking neural networks,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda A brain-inspired causal reasoning model based on spiking neural networks,

Reference 43

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Random features strengthen graph neural networks,

Reference 44

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This paper cites Stochastic Aggregation in Graph Neural Networks.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Stochastic Aggregation in Graph Neural Networks

Reference 45

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This paper cites Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning

Reference 46

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Emergence of scaling in random net- works,

Reference 47

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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Predicting missing links via local information,

Reference 48

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This paper cites Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction

Reference 49

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This paper cites Efficient reinforcement learning through evolving neural network topologies,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Efficient reinforcement learning through evolving neural network topologies,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:39:18.266640Z

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This paper cites De novo drug design using reinforcement learning with graph-based deep generative models,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda De novo drug design using reinforcement learning with graph-based deep generative models,

Reference 51

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This paper cites Graph networks for molecular design,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Graph networks for molecular design,

Reference 52

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This paper cites A purely spiking approach to reinforcement learning,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda A purely spiking approach to reinforcement learning,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:39:18.221316Z

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Observation 0da3a7fb-b90f-4844-b856-000c8313950d · outbound

This paper cites CausalLP: Learning causal relations with weighted knowledge graph link prediction.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda CausalLP: Learning causal relations with weighted knowledge graph link prediction

Reference 54

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Observation f9a5d2cf-7c55-4e04-8b39-b1b7474fae79 · outbound

This paper cites Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:39:18.207237Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c19f7d4-5ec3-4a83-b46f-aa2a5ec10277 · outbound

This paper cites Higher-order link prediction via light hypergraph neural network and hybrid aggregator,.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Higher-order link prediction via light hypergraph neural network and hybrid aggregator,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:39:18.192413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7edbfd29-1738-4f3c-b332-857b31dc8198 · outbound

This paper cites Temporal Knowledge Graph Completion: A Survey.

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Temporal Knowledge Graph Completion: A Survey

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T15:39:17.992342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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