Unveiling and Steering Connectome Organization with Interpretable Latent Variables | Paper Citation Record

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This paper cites Machine learning explainability in nasopharyngeal cancer survival using lime and shap.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Machine learning explainability in nasopharyngeal cancer survival using lime and shap

Reference 1

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This paper cites Tutorial cma-es: evolution strategies and covariance matrix adaptation.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Tutorial cma-es: evolution strategies and covariance matrix adaptation

Reference 2

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Dynamic programming

Reference 3

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables From the gene to behavior

Reference 4

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Eg-nas: neural architecture search with fast evolutionary exploration

Reference 5

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This paper cites A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing.Science advances, 8(44):eabq7592, 2022.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing.Science advances, 8(44):eabq7592, 2022

Reference 6

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This paper cites Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling

Reference 8

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This paper cites Flywire: online community for whole-brain connectomics.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Flywire: online community for whole-brain connectomics

Reference 9

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This paper cites What is dynamic programming? Nature biotechnology, 22(7):909–910, 2004.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables What is dynamic programming? Nature biotechnology, 22(7):909–910, 2004

Reference 10

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This paper cites Information theory and the genetic code.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Information theory and the genetic code

Reference 11

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This paper cites Sg-snn: a self-organizing spiking neural network based on temporal information.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Sg-snn: a self-organizing spiking neural network based on temporal information

Reference 12

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This paper cites Complex spiking neural networks with synaptic time-delay based on anti-interference function.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Complex spiking neural networks with synaptic time-delay based on anti-interference function

Reference 13

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This paper cites The CMA Evolution Strategy: A Tutorial.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables The CMA Evolution Strategy: A Tutorial

Reference 14

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Neuroscience-inspired artificial intelligence

Reference 15

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This paper cites Multi-level cognitive state classification of learners using complex brain networks and interpretable machine learning.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Multi-level cognitive state classification of learners using complex brain networks and interpretable machine learning

Reference 16

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This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 17

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This paper cites Improving Molecular Graph Generation with Flow Matching and Optimal Transport.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Improving Molecular Graph Generation with Flow Matching and Optimal Transport

Reference 19

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This paper cites Graph Generation with Diffusion Mixture.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Graph Generation with Diffusion Mixture

Reference 20

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This paper cites Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Reference 21

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This paper cites Generative ai: a review on models and applications.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Generative ai: a review on models and applications

Reference 22

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This paper cites Relevance Factor VAE: Learning and Identifying Disentangled Factors.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Relevance Factor VAE: Learning and Identifying Disentangled Factors

Reference 23

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Unresolved cited work

Reference 24

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This paper cites Auto-encoding variational bayes, 2013.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Auto-encoding variational bayes, 2013

Reference 25

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This paper cites Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Reference 26

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Deep learning

Reference 27

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This paper cites Dc-nas: Divide-and- conquer neural architecture search for multi-modal classification.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Dc-nas: Divide-and- conquer neural architecture search for multi-modal classification

Reference 28

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This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Challenging common assumptions in the unsupervised learning of disentangled representations

Reference 29

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This paper cites CMA-ES for Hyperparameter Optimization of Deep Neural Networks.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Reference 31

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This paper cites A unified approach to interpreting model predictions.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables A unified approach to interpreting model predictions

Reference 32

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This paper cites Graphdf: A discrete flow model for molecular graph generation.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Graphdf: A discrete flow model for molecular graph generation

Reference 33

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This paper cites Generative modelling of structurally constrained graphs.Advances in Neural Information Processing Systems, 37:137218– 137262, 2024.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Generative modelling of structurally constrained graphs.Advances in Neural Information Processing Systems, 37:137218– 137262, 2024

Reference 34

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This paper cites Toward an integration of deep learning and neuroscience.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Toward an integration of deep learning and neuroscience

Reference 35

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This paper cites From explanations to feature selection: assessing shap values as feature selection mechanism.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables From explanations to feature selection: assessing shap values as feature selection mechanism

Reference 36

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Algorithms for knapsack problems

Reference 37

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This paper cites Overlooked Implications of the Reconstruction Loss for VAE Disentanglement.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Overlooked Implications of the Reconstruction Loss for VAE Disentanglement

Reference 38

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This paper cites Computational role of structure in neural activity and connec- tivity.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Computational role of structure in neural activity and connec- tivity

Reference 39

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This paper cites Practical guide to shap analysis: explaining supervised machine learning model predictions in drug development.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Practical guide to shap analysis: explaining supervised machine learning model predictions in drug development

Reference 40

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This paper cites The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using shap.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using shap

Reference 41

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This paper cites Detection of the chronic kidney disease using xgboost classifier and explaining the influence of the attributes on the model using shap.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Detection of the chronic kidney disease using xgboost classifier and explaining the influence of the attributes on the model using shap

Reference 42

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This paper cites Connectome: How the brain’s wiring makes us who we are.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Connectome: How the brain’s wiring makes us who we are

Reference 43

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This paper cites GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Reference 44

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This paper cites Encoding innate ability through a genomic bottleneck.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Encoding innate ability through a genomic bottleneck

Reference 45

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This paper cites Graphvae: Towards generation of small graphs using variational autoencoders.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Graphvae: Towards generation of small graphs using variational autoencoders

Reference 46

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Networks of the brain

Reference 47

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This paper cites Bisnn: bio-information-fused spiking neural networks for enhanced eeg-based emotion recognition.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Bisnn: bio-information-fused spiking neural networks for enhanced eeg-based emotion recognition

Reference 48

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Graph Attention Networks

Reference 49

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This paper cites Discrete-state Continuous-time Diffusion for Graph Generation.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables Discrete-state Continuous-time Diffusion for Graph Generation

Reference 51

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This paper cites A critique of pure learning and what artificial neural networks can learn from animal brains.

Unveiling and Steering Connectome Organization with Interpretable Latent Variables A critique of pure learning and what artificial neural networks can learn from animal brains

Reference 52

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Unveiling and Steering Connectome Organization with Interpretable Latent Variables Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation

Reference 53

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