Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T11:12:43.778984Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2507.23010.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T11:12:43.778984Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T10:22:04.455412Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T10:24:06.819543Z
34 of 34 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 2b4666bb-3dc0-431b-ac94-eea9afd54ebe · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods GitHub - black-forest-labs/flux: Official inference repo for FLUX.1 models
Reference 1
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Observation 702430e7-ddde-4a7f-9978-ba6f357107e2 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Towards Evaluating the Robustness of Neural Networks
Reference 2
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Observation 2b842ac9-a7ad-40b6-8ae2-698c4a9348c2 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods M´ ethode g´ en´ erale pour la r´ esolution des systemes d’´ equations simultan´ ees
Reference 3
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Imagenet: A large-scale hierarchical image database
Reference 4
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 5
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Adaptive subgradient meth- ods for online learning and stochastic optimization
Reference 6
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Observation 4924bfe0-3397-477a-bcb6-29d14bd9cd5c · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Explaining and Harnessing Adversarial Examples
Reference 7
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Observation 45d9c584-c1e2-4b07-b03d-971df4852c1b · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Signal estimation from modified short-time Fourier transform
Reference 8
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Observation d34bb4df-7039-4ca3-b4b5-36b95a5edc58 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Reference 9
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Observation 310967cb-de3f-4244-834b-7d6a826c10c1 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Reference 10
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Inversion of neural networks by gradient descent
Reference 11
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Observation f8f2bf1f-0458-4b0a-bc97-6f792e72b05c · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Adam: A Method for Stochastic Optimization
Reference 12
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Observation 36e9a592-4559-4741-a599-b23883a2d057 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods ImageNet Clas- sification with Deep Convolutional Neural Networks
Reference 13
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Observation ab77093d-a728-44e0-b59a-2389c8319f8e · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Reference 14
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Observation 8027fa4b-481c-4526-972c-e9916c12c0a3 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Landscape Learning for Neural Network Inversion
Reference 15
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Observation e89fc5ad-9d40-4bc0-a6c0-d3d958bf03a9 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Decoupled Weight Decay Regularization
Reference 16
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Observation fc56bf93-c04a-47ad-8744-a3371e20c493 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Towards Deep Learning Models Resistant to Adversarial Attacks
Reference 17
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Observation dc9d1627-5433-42d4-9cf7-0d1d867c7571 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Efficient Estimation of Word Representations in Vector Space
Reference 18
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Observation b514ed5a-3545-48ea-b5c4-aa460887d442 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Unresolved cited work
Reference 19
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Glove: Global Vectors for Word Representation
Reference 20
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Some methods of speeding up the convergence of iter- ation methods
Reference 21
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Improving language understanding by generative pre- training
Reference 22
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Language models are unsupervised multitask learn- ers
Reference 23
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Observation 965fedae-912b-4a28-88f9-092edd4ab9c8 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Robust Speech Recognition via Large-Scale Weak Supervision
Reference 24
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Observation 95f31781-7373-4e50-beaa-f1b7ada5053c · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods GitHub - resemble-ai/chatterbox: SoTA open-source TTS
Reference 25
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Observation 3a8599e2-2f7f-48ec-94e5-2acc5e065263 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs
Reference 26
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Observation de3cbff6-6c6c-455c-935d-00dd207b3903 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods A stochastic approximation method
Reference 27
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Observation d0e2f940-3630-4547-af1e-f6e8312739c6 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Learning representations by back-propagating errors
Reference 28
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Observation 10c6b81a-677a-43b1-b990-fa31d10d1fc3 · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Intriguing properties of neural networks
Reference 29
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods BERTScore: Evaluating Text Generation with BERT
Reference 30
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Unresolved cited work
Reference 1543
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Observation 28d081f2-8cc6-473b-a8f2-20ea0f9e852b · outbound
Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Decoupled Weight Decay Regularization
Reference 2019
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods BERTScore: Evaluating Text Generation with BERT
Reference 2020
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Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods Unresolved cited work
Reference 2025
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Observation 9e9ac3cc-0312-4687-a458-bdf9a3708f7d · inbound
Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methods
Reference 42
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