Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum | Paper Citation Record

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

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This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 2

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This paper cites IEEE/ACM Transactions on Audio, Speech, and Language Processing29, 745–755 (2021).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum IEEE/ACM Transactions on Audio, Speech, and Language Processing29, 745–755 (2021)

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This paper cites Neural computation 9(8), 1735–1780 (1997).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Neural computation 9(8), 1735–1780 (1997)

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This paper cites In: Proceedings, vol.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings, vol

Reference 5

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 6

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This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 7

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This paper cites Language Modeling with Deep Transformers.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Language Modeling with Deep Transformers

Reference 8

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

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems28 (2015)

Reference 9

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This paper cites Exploring Sparsity in Recurrent Neural Networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Exploring Sparsity in Recurrent Neural Networks

Reference 10

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This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 11

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This paper cites The State of Sparsity in Deep Neural Networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum The State of Sparsity in Deep Neural Networks

Reference 12

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This paper cites Neurocomputing390, 327–340 (2020) 20.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Neurocomputing390, 327–340 (2020) 20

Reference 13

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This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 14

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This paper cites Soft Weight-Sharing for Neural Network Compression.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Soft Weight-Sharing for Neural Network Compression

Reference 15

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This paper cites International Journal of Computer Vision129, 1789–1819 (2021).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum International Journal of Computer Vision129, 1789–1819 (2021)

Reference 16

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This paper cites Lyapunov-Guided Representation of Recurrent Neural Network Performance.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Lyapunov-Guided Representation of Recurrent Neural Network Performance

Reference 17

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This paper cites Journal of Machine Learning Research22(241), 1–124 (2021).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Journal of Machine Learning Research22(241), 1–124 (2021)

Reference 18

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This paper cites arXiv e-prints, 2103 (2021).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum arXiv e-prints, 2103 (2021)

Reference 19

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This paper cites Physical Review A 39(12), 6600 (1989).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Physical Review A 39(12), 6600 (1989)

Reference 20

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This paper cites Connection Science1(1), 3–16 (1989).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Connection Science1(1), 3–16 (1989)

Reference 21

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This paper cites Advances in neural information processing systems1(1988).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems1(1988)

Reference 22

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This paper cites Advances in neural information processing systems2(1989).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems2(1989)

Reference 23

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This paper cites Advances in neural information processing systems5(1992).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems5(1992)

Reference 24

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Pruning Filters for Efficient ConvNets

Reference 25

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

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems29(2016)

Reference 26

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This paper cites In: Proceedings of the European Conference on Computer Vision (ECCV), pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the European Conference on Computer Vision (ECCV), pp

Reference 27

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This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 28

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 29

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 30

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 31

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 32

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 33

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This paper cites Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers

Reference 34

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 35

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Dynamic Model Pruning with Feedback

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Learning Sparse Neural Networks through $L_0$ Regularization

Reference 37

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Learning Intrinsic Sparse Structures within Long Short-Term Memory

Reference 38

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 39

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

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 40

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum SNIP: Single-shot Network Pruning based on Connection Sensitivity

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum A Signal Propagation Perspective for Pruning Neural Networks at Initialization

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This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Picking Winning Tickets Before Training by Preserving Gradient Flow

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This paper cites Advances in Neural Information Processing Systems33, 6377–6389 (2020).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in Neural Information Processing Systems33, 6377–6389 (2020)

Reference 44

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This paper cites Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks

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This paper cites Deep Rewiring: Training very sparse deep networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Deep Rewiring: Training very sparse deep networks

Reference 46

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This paper cites IEEE Transactions on Computers68(10), 1487–1497 (2019).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum IEEE Transactions on Computers68(10), 1487–1497 (2019)

Reference 47

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This paper cites Nature communications9(1), 1–12 (2018).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Nature communications9(1), 1–12 (2018)

Reference 48

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 49

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This paper cites Sparse Networks from Scratch: Faster Training without Losing Performance.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Sparse Networks from Scratch: Faster Training without Losing Performance

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This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 51

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This paper cites Advances in Neural Information Processing Systems33, 20744–20754 (2020).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in Neural Information Processing Systems33, 20744–20754 (2020)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.852671Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61a5c83f-d549-4bd3-bd88-d55cc84b5f69 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.842476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7f676979-0471-4f67-8d53-952670ca7b16 · outbound

This paper cites Pruning Neural Networks at Initialization: Why are We Missing the Mark?.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.138617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 425133a8-8ad5-46ec-9f20-66cb3ec8d1e6 · outbound

This paper cites Journal of machine learning research13(2) (2012).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Journal of machine learning research13(2) (2012)

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.141835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 287138f6-1fb8-4d4e-9050-3178556e00d4 · outbound

This paper cites Advances in neural information processing systems 25(2012).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems 25(2012)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.826620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 183c3fbd-1f08-4652-b4bf-a6299fb1b189 · outbound

This paper cites In: International Conference on Learning and Intelligent Optimization, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Learning and Intelligent Optimization, pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.816586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b0d9ef17-f003-4c6c-9c63-09ba2279ac2c · outbound

This paper cites Advances in neural information processing systems24(2011).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Advances in neural information processing systems24(2011)

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.150908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a442caa2-c5af-4948-b4d4-3ea4d9175ebc · outbound

This paper cites In: Pro- ceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Pro- ceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.800739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 77e8b165-9fa6-442f-86e5-0d7e7bf0b2ca · outbound

This paper cites In: NIPS Workshop on Bayesian Optimization in Theory and Practice (2013).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: NIPS Workshop on Bayesian Optimization in Theory and Practice (2013)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.791689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6634c491-e46b-4422-bcf9-7d7e251c494c · outbound

This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.782474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 602a876e-56e2-4ef4-a3cc-9e7018da9454 · outbound

This paper cites Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.163513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 106e7eeb-4167-481b-8643-00f92ec7be24 · outbound

This paper cites The journal of machine learning research18(1), 6765–6816 (2017).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum The journal of machine learning research18(1), 6765–6816 (2017)

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.773230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bdfd1587-4abe-4c99-8643-f13ab8dad709 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Machine Learning, pp

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.764069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85581ed9-7284-4859-adc2-c471a900c7c8 · outbound

This paper cites AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 65

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:27:08.380777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 395a8259-1cde-4faa-aed5-a51fde0a02f2 · outbound

This paper cites R-FORCE: Robust Learning for Random Recurrent Neural Networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum R-FORCE: Robust Learning for Random Recurrent Neural Networks

Reference 66

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:27:08.368080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ab05760-def6-4ea9-9513-cbbd40cb2c88 · outbound

This paper cites Frontiers in Applied Mathematics and Statistics8(2022) https://doi.org/ 10.3389/fams.2022.818799.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Frontiers in Applied Mathematics and Statistics8(2022) https://doi.org/ 10.3389/fams.2022.818799

Reference 67

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:27:08.354378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 604c89c0-cc31-448b-8db2-fb373b4257f7 · outbound

This paper cites In: Interna- tional Conference on Artificial Intelligence and Statistics, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Interna- tional Conference on Artificial Intelligence and Statistics, pp

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.754901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04ae4e6d-f3b2-499c-9ade-c6c8859b28d5 · outbound

This paper cites Physical review letters105(26), 268104 (2010).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Physical review letters105(26), 268104 (2010)

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.745737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e004e9e-d2d6-490f-89fc-c86ca890ff14 · outbound

This paper cites Lyapunov spectra of chaotic recurrent neural networks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Lyapunov spectra of chaotic recurrent neural networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.189700Z

Source-reported events for the cited work

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Observation af647e8e-b834-4366-a184-81d7fe73cabb · outbound

This paper cites Dynamical systems, 1–43 (1995).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Dynamical systems, 1–43 (1995)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.737129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c361a374-d87f-4596-86df-431f74120eb6 · outbound

This paper cites In: Stochastic Behavior in Classical and Quantum Hamiltonian Systems, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: Stochastic Behavior in Classical and Quantum Hamiltonian Systems, pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.727981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 691b3f34-0523-4436-8700-91915a59bd99 · outbound

This paper cites Dynamics and Stability of Systems14(2), 183–201 (1999).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Dynamics and Stability of Systems14(2), 183–201 (1999)

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.718029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 74ba21a6-cfdb-48d4-a0f8-887bc6f7c9c9 · outbound

This paper cites Neural networks20(3), 323–334 (2007).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Neural networks20(3), 323–334 (2007)

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.708797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8eb01e95-dc13-4c57-816e-6066236a44b4 · outbound

This paper cites In: International Conference on Artificial Intelligence and Statistics, pp.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum In: International Conference on Artificial Intelligence and Statistics, pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.699206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fba2e746-70a6-4557-81fb-d34e43992eee · outbound

This paper cites A recurrent neural network without chaos.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum A recurrent neural network without chaos

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.208585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 883782a3-46ec-452c-bcb4-082130731bc6 · outbound

This paper cites Physical review letters73(14), 1927 (1994).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Physical review letters73(14), 1927 (1994)

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.689798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1212f13f-efea-4a49-89aa-d0917437bfb4 · outbound

This paper cites Journal of Nonlinear Science1(2), 175–199 (1991).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Journal of Nonlinear Science1(2), 175–199 (1991)

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.679918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b0d3a7d-e244-4a4d-bfa4-43473c76ead4 · outbound

This paper cites Physica A: Statistical Mechanics and its Applications292(1-4), 182–192 (2001) 25.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Physica A: Statistical Mechanics and its Applications292(1-4), 182–192 (2001) 25

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.668803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a98185ca-f7c6-4fcf-8e06-2e5fe879ae6b · outbound

This paper cites Physical Review Letters51(16), 1442 (1983).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Physical Review Letters51(16), 1442 (1983)

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.657945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 33cc186f-7315-43e5-8e36-50b91af5bddb · outbound

This paper cites Progress of theoretical physics79(6), 1265–1268 (1988).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Progress of theoretical physics79(6), 1265–1268 (1988)

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.648045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cde96d7c-107b-4595-b262-20184f935129 · outbound

This paper cites Neural Computing and Applications36(34), 21211– 21226 (2024).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Neural Computing and Applications36(34), 21211– 21226 (2024)

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.638528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 43e2bd5c-5c17-43cc-9cf6-36a5aac76a76 · outbound

This paper cites Using Large Corpora, 273 (1994).

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Using Large Corpora, 273 (1994)

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:27:08.628323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61163268-4c37-46d3-800f-ffc5c18ba489 · outbound

This paper cites Pointer Sentinel Mixture Models.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Pointer Sentinel Mixture Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.235260Z

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