Paper Citation Record · LEDGER

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.29463.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.29463 v1

Coverage vector

measured 47 of 47 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 003fd374-ae19-47c9-8f42-d8bb84e44c52 · outbound

This paper cites In: The Eleventh International Conference on Learning Rep- resentations (2023).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: The Eleventh International Conference on Learning Rep- resentations (2023)

Reference 1

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This paper cites PloS one10(7), e0130140 (2015).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification PloS one10(7), e0130140 (2015)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Spatial Functa: Scaling Functa to ImageNet Classification and Generation

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This paper cites IEEE Transactions on Knowledge and Data Engineering (2025).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification IEEE Transactions on Knowledge and Data Engineering (2025)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Deep Learning on Implicit Neural Representations of Shapes

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: 2009 IEEE conference on computer vision and pattern recognition

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: International Conference on Machine Learning

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: International conference on machine learning

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the IEEE/CVF international conference on computer vision

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the IEEE international conference on computer vision

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This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 12

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: International Conference on Machine Learning

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the Computer Vision and Pattern Recognition Confer- ence

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This paper cites Advances in Neural Information Processing Systems35, 11893–11905 (2022).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in Neural Information Processing Systems35, 11893–11905 (2022)

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This paper cites In: International Conference on Machine Learning.

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: International Conference on Machine Learning

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This paper cites Advances in neural information processing systems28 (2015).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in neural information processing systems28 (2015)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Mathematical Models of Computation in Superposition

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Information11(2), 108 (2020)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: The Thirteenth International Conference on Learning Representations (2025)

Reference 20

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Scale Equivariant Graph Metanetworks

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: The Twelfth International Conference on Learning Repre- sentations (2024)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Proceedings of the IEEE86(11), 2278–2324 (1998)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Graph Metanetworks for Processing Diverse Neural Architectures

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Decoupled Weight Decay Regularization

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification arXiv preprint arXiv:2406.17438 (2024)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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This paper cites Advances in neural information processing systems29(2016).

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in neural information processing systems29(2016)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Distill5(3), e00024–001 (2020)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the British Machine Vision Conference (BMVC) (2018)

Reference 33

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in Neural Information Processing Systems34, 8583–8595 (2021)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the IEEE international conference on computer vision

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Scaling Vision-Language Models with Sparse Mixture of Experts

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in neural information processing systems33, 7462–7473 (2020)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification SmoothGrad: removing noise by adding noise

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in neural informa- tion processing systems33, 7537–7547 (2020)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification Advances in neural information processing systems36(2024)

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Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification For the inner-loop optimization, we use SGD without momentum, and we optimize the inner-loop learning rates using AdamW with a learning rate of 0.01

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