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Paper Citation Record · LEDGER

Geometric and Information Compression of Representations in Deep Learning

As of 10 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2606.21593.

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

pith.paper-citation-record.v1
2606.21593 v2

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measured 60 of 60 reference resolution

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measured 60 of 60 standing notices

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measured 0 of 0 inbound itemization

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Reference resolution

60 of 60 outbound references displayed

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Outbound references

Observation 073252d2-c450-4b4d-b220-50c18f0061d3 · outbound

This paper cites IEEE transactions on pattern analysis and machine intelligence40(12), 2897–2905 (2018).

Geometric and Information Compression of Representations in Deep Learning IEEE transactions on pattern analysis and machine intelligence40(12), 2897–2905 (2018)

Reference 1

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This paper cites In: The Eleventh International Conference on Learning Representations (2023).

Geometric and Information Compression of Representations in Deep Learning In: The Eleventh International Conference on Learning Representations (2023)

Reference 2

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This paper cites Advances in Neural Information Processing Systems35, 17626–17638 (2022).

Geometric and Information Compression of Representations in Deep Learning Advances in Neural Information Processing Systems35, 17626–17638 (2022)

Reference 3

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This paper cites In: International Conference on Learning Representations (2017).

Geometric and Information Compression of Representations in Deep Learning In: International Conference on Learning Representations (2017)

Reference 4

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence42(9), 2225–2239 (2019).

Geometric and Information Compression of Representations in Deep Learning IEEE Transactions on Pattern Analysis and Machine Intelligence42(9), 2225–2239 (2019)

Reference 5

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This paper cites In: International Conference on Learning Representations (2018).

Geometric and Information Compression of Representations in Deep Learning In: International Conference on Learning Representations (2018)

Reference 6

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 7

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This paper cites Advances in Neural Information Processing Systems31(2018).

Geometric and Information Compression of Representations in Deep Learning Advances in Neural Information Processing Systems31(2018)

Reference 8

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This paper cites In: Advances in Neural Information Processing Systems.

Geometric and Information Compression of Representations in Deep Learning In: Advances in Neural Information Processing Systems

Reference 9

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This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Geometric and Information Compression of Representations in Deep Learning In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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This paper cites In: International Conference on Machine Learning.

Geometric and Information Compression of Representations in Deep Learning In: International Conference on Machine Learning

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This paper cites In: International conference on machine learning.

Geometric and Information Compression of Representations in Deep Learning In: International conference on machine learning

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This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Geometric and Information Compression of Representations in Deep Learning Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 13

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Geometric and Information Compression of Representations in Deep Learning In: Proc

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Geometric and Information Compression of Representations in Deep Learning Entropy22(9), 999 (2020)

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Geometric and Information Compression of Representations in Deep Learning In: International Conference on Learning Representations (2021)

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Geometric and Information Compression of Representations in Deep Learning In: International conference on machine learning

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Geometric and Information Compression of Representations in Deep Learning Entropy22(11), 1229 (Nov 2020), open-access

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Geometric and Information Compression of Representations in Deep Learning IEEE Transactions on Neural Networks and Learning Systems33(12), 7039–7051 (Dec 2022)

Reference 19

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Geometric and Information Compression of Representations in Deep Learning Journal of Machine Learning Research3(Mar), 1307–1331 (2003)

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Geometric and Information Compression of Representations in Deep Learning In: International Conference on Machine Learning (2019)

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Geometric and Information Compression of Representations in Deep Learning Advances in neural information processing systems30(2017)

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Geometric and Information Compression of Representations in Deep Learning In: Advances in Neural Information Processing Systems (2025)

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Geometric and Information Compression of Representations in Deep Learning In: Proceedings of the IEEE conference on computer vision and pattern recognition

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Geometric and Information Compression of Representations in Deep Learning In: Proceedings of the IEEE conference on computer vision and pattern recognition

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Geometric and Information Compression of Representations in Deep Learning In: International Conference on Learning Representations (2019)

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Geometric and Information Compression of Representations in Deep Learning In: Interna- tional Conference on Learning Representations (2016)

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Geometric and Information Compression of Representations in Deep Learning In: International Conference on Learning Representations (2019)

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Geometric and Information Compression of Representations in Deep Learning Birkhäuser Boston, MA (2012)

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Geometric and Information Compression of Representations in Deep Learning Proceedings of the IEEE86(11), 2278–2324 (1998)

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Geometric and Information Compression of Representations in Deep Learning In: The 22nd international conference on artificial intelligence and statistics

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Geometric and Information Compression of Representations in Deep Learning In: Pro- ceedings of the 2022 SIAM International Conference on Data Mining (SDM)

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Geometric and Information Compression of Representations in Deep Learning In: International Conference on Artificial Intelligence and Statistics

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Geometric and Information Compression of Representations in Deep Learning In: Advances in Neural Information Processing Systems

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Geometric and Information Compression of Representations in Deep Learning Representation Learning with Contrastive Predictive Coding

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Geometric and Information Compression of Representations in Deep Learning Proceedings of the National Academy of Sciences117(40), 24652–24663 (2020)

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Geometric and Information Compression of Representations in Deep Learning In: ICLR 2026 Workshop on Geometry-grounded Representation Learning and Generative Modeling (2026)

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Geometric and Information Compression of Representations in Deep Learning In: Workshop on Machine Learning and Compression @ NeurIPS (2024)

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Observation da6ba62d-87c9-4ce3-8b73-79689a558f94 · outbound

This paper cites Advances in neural information processing systems34, 18420–18432 (2021).

Geometric and Information Compression of Representations in Deep Learning Advances in neural information processing systems34, 18420–18432 (2021)

Reference 39

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Observation b92708da-b677-4399-b031-949044eacfdd · outbound

This paper cites Multi-Task Variational Information Bottleneck.

Geometric and Information Compression of Representations in Deep Learning Multi-Task Variational Information Bottleneck

Reference 40

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Observation d3bf00ac-b829-4514-a6e6-ebd7f171de7b · outbound

This paper cites IEEE Transactions on Information Forensics and Security18, 2060–2075 (2023).

Geometric and Information Compression of Representations in Deep Learning IEEE Transactions on Information Forensics and Security18, 2060–2075 (2023)

Reference 41

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 42

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Observation e6723ad4-55f4-4fa8-b51b-5b132c3e3a83 · outbound

This paper cites Explaining grokking and information bottleneck through neural collapse emergence.

Geometric and Information Compression of Representations in Deep Learning Explaining grokking and information bottleneck through neural collapse emergence

Reference 43

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Observation 1e4380c6-595c-4258-9db4-45205ec7d24b · outbound

This paper cites In: Proc.

Geometric and Information Compression of Representations in Deep Learning In: Proc

Reference 44

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Observation 4dba9d47-b01c-47dc-a1c8-7ef3157a0c84 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

Geometric and Information Compression of Representations in Deep Learning Opening the Black Box of Deep Neural Networks via Information

Reference 45

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Observation aaf0474e-fdee-4bd7-9ab0-3dd302e76376 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Geometric and Information Compression of Representations in Deep Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 46

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Observation 00f37a4d-1a5b-4c91-aaa1-989c48156bca · outbound

This paper cites In: Workshop on Machine Learning and Compression @ NeurIPS (2024).

Geometric and Information Compression of Representations in Deep Learning In: Workshop on Machine Learning and Compression @ NeurIPS (2024)

Reference 47

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Observation 09104f34-ed42-444f-a6a2-16497e7f2f59 · outbound

This paper cites The journal of machine learning research15(1), 1929–1958 (2014).

Geometric and Information Compression of Representations in Deep Learning The journal of machine learning research15(1), 1929–1958 (2014)

Reference 48

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Observation ca8b2d3f-9ec1-4b31-b6cc-096821f93aea · outbound

This paper cites Well-Read Students Learn Better: On the Importance of Pre-training Compact Models.

Geometric and Information Compression of Representations in Deep Learning Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Reference 49

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Observation 0cbe8106-09a0-4dcc-8d8c-2c91e19d2bb5 · outbound

This paper cites Differential and Combinatorial Topology, Princeton Univ Press pp.

Geometric and Information Compression of Representations in Deep Learning Differential and Combinatorial Topology, Princeton Univ Press pp

Reference 50

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Observation a0957e28-6d3e-496f-8634-624727577867 · outbound

This paper cites In: British Machine Vision Conference 2016.

Geometric and Information Compression of Representations in Deep Learning In: British Machine Vision Conference 2016

Reference 51

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Observation 0235bf0a-2624-45e5-931c-42b260013c72 · outbound

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 52

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 53

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 54

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Observation cb29942c-9f4a-40b9-9436-37e13fcc9961 · outbound

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 55

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Observation a1d9d5c8-017b-44e5-8831-93ccaee714c5 · outbound

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 56

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Observation ec89022a-2d6e-47ca-b0d2-571ca7ca3c26 · outbound

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Geometric and Information Compression of Representations in Deep Learning Unresolved cited work

Reference 57

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Observation 99775cc2-c1df-464e-9e27-4da051d28fea · outbound

This paper cites logistic.

Geometric and Information Compression of Representations in Deep Learning logistic

Reference 58

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Observation 30189890-6a3f-4359-9daf-177f5da81e7a · outbound

This paper cites 1(a)), which indicates that noise causes class-specific distributions to overlap.

Geometric and Information Compression of Representations in Deep Learning 1(a)), which indicates that noise causes class-specific distributions to overlap

Reference 59

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Observation ff1c9c8f-a310-463e-9a11-bace0f0e8eb4 · outbound

This paper cites 1(a)), which indicates that samples from the same class are mapped closely in latent space, at least via the deterministic part ofµ(x)of the encoder.

Geometric and Information Compression of Representations in Deep Learning 1(a)), which indicates that samples from the same class are mapped closely in latent space, at least via the deterministic part ofµ(x)of the encoder

Reference 60

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Pith citing papers

No inbound Pith citation observations are available.