Aligning Multimodal Representations through an Information Bottleneck | Paper Citation Record

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This paper cites and Soatto, S.

Aligning Multimodal Representations through an Information Bottleneck and Soatto, S

Reference 1

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Observation 10dd87b3-f69c-43d8-a95c-a42e47136f6f · outbound

This paper cites and Soatto, S.

Aligning Multimodal Representations through an Information Bottleneck and Soatto, S

Reference 2

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This paper cites Deep Variational Information Bottleneck.

Aligning Multimodal Representations through an Information Bottleneck Deep Variational Information Bottleneck

Reference 3

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This paper cites D., and Buchwalter, W.

Aligning Multimodal Representations through an Information Bottleneck D., and Buchwalter, W

Reference 4

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This paper cites Revisiting model stitching to compare neural representations.

Aligning Multimodal Representations through an Information Bottleneck Revisiting model stitching to compare neural representations

Reference 5

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This paper cites Representation learning: A review and new perspectives.

Aligning Multimodal Representations through an Information Bottleneck Representation learning: A review and new perspectives

Reference 6

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This paper cites and Kim, H.

Aligning Multimodal Representations through an Information Bottleneck and Kim, H

Reference 7

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This paper cites F., Hong, H., Yamins, D.

Aligning Multimodal Representations through an Information Bottleneck F., Hong, H., Yamins, D

Reference 8

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This paper cites A simple framework for contrastive learning of visual representations.

Aligning Multimodal Representations through an Information Bottleneck A simple framework for contrastive learning of visual representations

Reference 9

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This paper cites Uniter: Universal image-text representation learning.

Aligning Multimodal Representations through an Information Bottleneck Uniter: Universal image-text representation learning

Reference 10

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This paper cites Inductive Bias of Deep Convolutional Networks through Pooling Geometry.

Aligning Multimodal Representations through an Information Bottleneck Inductive Bias of Deep Convolutional Networks through Pooling Geometry

Reference 11

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This paper cites Algorithms for learning kernels based on centered alignment.

Aligning Multimodal Representations through an Information Bottleneck Algorithms for learning kernels based on centered alignment

Reference 12

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This paper cites On kernel-target alignment.

Aligning Multimodal Representations through an Information Bottleneck On kernel-target alignment

Reference 13

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This paper cites Scaling Properties of Speech Language Models.

Aligning Multimodal Representations through an Information Bottleneck Scaling Properties of Speech Language Models

Reference 14

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

Reference 15

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Aligning Multimodal Representations through an Information Bottleneck BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

Reference 17

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Aligning Multimodal Representations through an Information Bottleneck An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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This paper cites It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap.

Aligning Multimodal Representations through an Information Bottleneck It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap

Reference 19

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This paper cites Eva: Exploring the limits of masked visual representation learning at scale.

Aligning Multimodal Representations through an Information Bottleneck Eva: Exploring the limits of masked visual representation learning at scale

Reference 20

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Observation eabf118d-8a0b-4e23-bae2-ba3d8541afaa · outbound

This paper cites Learning Robust Representations via Multi-View Information Bottleneck.

Aligning Multimodal Representations through an Information Bottleneck Learning Robust Representations via Multi-View Information Bottleneck

Reference 21

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This paper cites Towards artificial general intelligence via a multimodal foundation model.

Aligning Multimodal Representations through an Information Bottleneck Towards artificial general intelligence via a multimodal foundation model

Reference 22

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This paper cites S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., and Mikolov, T.

Aligning Multimodal Representations through an Information Bottleneck S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., and Mikolov, T

Reference 23

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This paper cites V., Joulin, A., and Misra, I.

Aligning Multimodal Representations through an Information Bottleneck V., Joulin, A., and Misra, I

Reference 24

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This paper cites W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Sch \"o lkopf, B., and Bauer, S.

Aligning Multimodal Representations through an Information Bottleneck W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Sch \"o lkopf, B., and Bauer, S

Reference 25

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This paper cites Deep residual learning for image recognition.

Aligning Multimodal Representations through an Information Bottleneck Deep residual learning for image recognition

Reference 26

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This paper cites Data-efficient image recognition with contrastive predictive coding.

Aligning Multimodal Representations through an Information Bottleneck Data-efficient image recognition with contrastive predictive coding

Reference 27

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This paper cites Learning deep representations by mutual information estimation and maximization.

Aligning Multimodal Representations through an Information Bottleneck Learning deep representations by mutual information estimation and maximization

Reference 28

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This paper cites The Platonic Representation Hypothesis.

Aligning Multimodal Representations through an Information Bottleneck The Platonic Representation Hypothesis

Reference 29

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This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Aligning Multimodal Representations through an Information Bottleneck Scaling up visual and vision-language representation learning with noisy text supervision

Reference 30

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This paper cites Scaling Laws for Neural Language Models.

Aligning Multimodal Representations through an Information Bottleneck Scaling Laws for Neural Language Models

Reference 31

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Aligning Multimodal Representations through an Information Bottleneck and Fei-Fei, L

Reference 32

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Aligning Multimodal Representations through an Information Bottleneck Similarity of Neural Network Models: A Survey of Functional and Representational Measures

Reference 33

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Aligning Multimodal Representations through an Information Bottleneck Multimodal machine learning in precision health: A scoping review

Reference 34

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Aligning Multimodal Representations through an Information Bottleneck Similarity of neural network representations revisited

Reference 35

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

Reference 36

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This paper cites Multimodality of AI for Education: Towards Artificial General Intelligence.

Aligning Multimodal Representations through an Information Bottleneck Multimodality of AI for Education: Towards Artificial General Intelligence

Reference 37

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

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Aligning Multimodal Representations through an Information Bottleneck Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 39

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Aligning Multimodal Representations through an Information Bottleneck Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 40

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Aligning Multimodal Representations through an Information Bottleneck W., Zhang, Y., Kwon, Y., Yeung, S., and Zou, J

Reference 41

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

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This paper cites An efficient framework for learning sentence representations.

Aligning Multimodal Representations through an Information Bottleneck An efficient framework for learning sentence representations

Reference 43

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Aligning Multimodal Representations through an Information Bottleneck dsprites: Disentanglement testing sprites dataset

Reference 44

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

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Aligning Multimodal Representations through an Information Bottleneck Representation Learning with Contrastive Predictive Coding

Reference 46

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Aligning Multimodal Representations through an Information Bottleneck Bleu: a method for automatic evaluation of machine translation

Reference 47

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Aligning Multimodal Representations through an Information Bottleneck W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 48

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Aligning Multimodal Representations through an Information Bottleneck Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability

Reference 49

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This paper cites Do vision transformers see like convolutional neural networks? Advances in neural information processing systems, 34: 0 12116--12128, 2021.

Aligning Multimodal Representations through an Information Bottleneck Do vision transformers see like convolutional neural networks? Advances in neural information processing systems, 34: 0 12116--12128, 2021

Reference 50

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Aligning Multimodal Representations through an Information Bottleneck Accept the modality gap: An exploration in the hyperbolic space

Reference 51

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Aligning Multimodal Representations through an Information Bottleneck Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 52

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This paper cites Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models.

Aligning Multimodal Representations through an Information Bottleneck Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

Reference 53

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Aligning Multimodal Representations through an Information Bottleneck Facenet: A unified embedding for face recognition and clustering

Reference 54

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Aligning Multimodal Representations through an Information Bottleneck Opening the Black Box of Deep Neural Networks via Information

Reference 55

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Aligning Multimodal Representations through an Information Bottleneck Human computer interaction

Reference 56

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Aligning Multimodal Representations through an Information Bottleneck Contrastive multiview coding

Reference 57

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Aligning Multimodal Representations through an Information Bottleneck What makes for good views for contrastive learning? Advances in neural information processing systems, 33: 0 6827--6839, 2020 b

Reference 58

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This paper cites and Zaslavsky, N.

Aligning Multimodal Representations through an Information Bottleneck and Zaslavsky, N

Reference 59

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Aligning Multimodal Representations through an Information Bottleneck The information bottleneck method

Reference 60

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Aligning Multimodal Representations through an Information Bottleneck Self-supervised Learning from a Multi-view Perspective

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:52.837965Z

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Aligning Multimodal Representations through an Information Bottleneck Understanding and fixing the modality gap in vision-language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:56.710884Z

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This paper cites Ranking with ordered weighted pairwise classification.

Aligning Multimodal Representations through an Information Bottleneck Ranking with ordered weighted pairwise classification

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:56.503496Z

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Aligning Multimodal Representations through an Information Bottleneck Cider: Consensus-based image description evaluation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.034469Z

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Observation 201d5b3b-ef90-4609-97d8-0e10bc5204f4 · outbound

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Aligning Multimodal Representations through an Information Bottleneck and Liu, H

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:56.271112Z

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This paper cites Rethinking minimal sufficient representation in contrastive learning.

Aligning Multimodal Representations through an Information Bottleneck Rethinking minimal sufficient representation in contrastive learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:55.983477Z

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Observation ec1373d0-d0a9-44f5-83eb-6b7116d18087 · outbound

This paper cites and Isola, P.

Aligning Multimodal Representations through an Information Bottleneck and Isola, P

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.333233Z

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Observation 133e3845-c0d2-438c-bba2-c5335b980b1f · outbound

This paper cites Disentangled representation learning.

Aligning Multimodal Representations through an Information Bottleneck Disentangled representation learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:55.805732Z

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This paper cites and Wu, L.

Aligning Multimodal Representations through an Information Bottleneck and Wu, L

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:55.642412Z

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This paper cites Large scale image annotation: learning to rank with joint word-image embeddings.

Aligning Multimodal Representations through an Information Bottleneck Large scale image annotation: learning to rank with joint word-image embeddings

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:43:55.392339Z

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This paper cites X., and Lin, D.

Aligning Multimodal Representations through an Information Bottleneck X., and Lin, D

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.721604Z

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Observation ac0b7945-438d-4213-8433-b61b1623e624 · outbound

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:43:55.145579Z

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Observation 1d03ffe8-d8c3-4558-9a22-81ccdd0cfdac · outbound

This paper cites VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

Aligning Multimodal Representations through an Information Bottleneck VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.900356Z

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Observation ccc34d92-69f0-4757-ae50-eb8e762be252 · outbound

This paper cites A Theory of Usable Information Under Computational Constraints.

Aligning Multimodal Representations through an Information Bottleneck A Theory of Usable Information Under Computational Constraints

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.975431Z

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Observation badf7ca8-b6ac-4039-878e-f64828ad176c · outbound

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Aligning Multimodal Representations through an Information Bottleneck Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:43:54.922513Z

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Observation 6db1ae11-4405-4f9e-b140-f5941cf85b96 · outbound

This paper cites write newline.

Aligning Multimodal Representations through an Information Bottleneck write newline

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.223613Z

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