RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer | Paper Citation Record

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This paper cites VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Understanding of a convolutional neural network

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Neural Machine Translation by Jointly Learning to Align and Translate

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Bartlett and Shahar Mendelson

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

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This paper cites Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Ehrenfeucht, David Haussler, and Manfred K

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Purushotham, Kyunghyun Cho, David A

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Pix2seq: A Language Modeling Framework for Object Detection

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Learning phrase representations using rnn en- coder–decoder for statistical machine translation

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Convolutional neural networks on graphs with fast localized spectral filtering

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Bert: Pre-training of deep bidirectional transformers for language understanding

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Transductive rademacher complexity and its applications

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Th´eorie analytique de la chaleur

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Garg, Stefanie Jegelka, and T

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer node2vec: Scalable feature learning for networks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Zhang, Shaoqing Ren, and Jian Sun

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Denoising Diffusion Probabilistic Models

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Video Diffusion Models

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Long short-term memory

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Gpt-gnn: Gen- erative pre-training of graph neural networks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Le, Yun- Hsuan Sung, Zhen Li, and Tom Duerig

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Johnson and Joram Lindenstrauss

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Convolutional neural networks for sentence classification

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Semi-Supervised Classification with Graph Convolutional Networks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer VideoPoet: A Large Language Model for Zero-Shot Video Generation

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer VeRA: Vector-based Random Matrix Adaptation

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Imagenet classification with deep convolutional neural networks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Dense Associative Memory for Pattern Recognition

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Large Associative Memory Problem in Neurobiology and Machine Learning

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Lecun, L

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Convolutional networks for images, speech, and time series, page 255–258

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer VisualBERT: A Simple and Performant Baseline for Vision and Language

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Deeper insights into graph convolutional net- works for semi-supervised learning

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Vilbert: Pretraining task-agnostic vi- siolinguistic representations for vision-and-language tasks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Recur- rent models of visual attention

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Dreamix: Video Diffusion Models are General Video Editors

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Glide: Towards photorealistic image generation and editing with text-guided diffusion models

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer An Introduction to Convolutional Neural Networks

Reference 55

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Peebles and Saining Xie

Reference 56

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Reference 57

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Qi, Hao Su, Kaichun Mo, and Leonidas J

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.862155Z

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Learning transferable visual models from natural language supervision

Reference 59

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Reference 60

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Reference 61

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Hopfield Networks is All You Need

Reference 62

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer

Reference 63

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Dropedge: Towards deep graph convolutional networks on node classification

Reference 64

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Perceptual generalization over transformation groups

Reference 65

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Ross, Jongwoo Lim, Ruei-Sung Lin, and Ming-Hsuan Yang

Reference 66

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This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 67

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Schuster and K.K

Reference 68

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Observation 267a9249-4eec-4054-a1d7-9ef6d3f40bd4 · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 69

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 70

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Flava: A foundational language and vision alignment model

Reference 71

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This paper cites Yu, and Tianyi Wu.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Yu, and Tianyi Wu

Reference 72

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Lxmert: Learning cross-modality encoder representations from transformers

Reference 73

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Observation b6986231-0297-49e3-afee-d9f9eb23958c · outbound

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Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:48.757496Z

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 75

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Graph Attention Networks

Reference 76

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Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.744149Z

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Observation baabd22f-59a3-4cb8-94ae-b8ef35f49b9a · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion

Reference 78

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Residual attention network for image classification

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.735502Z

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Observation c676c29d-c340-4d31-84ed-558ab0b67770 · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 80

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Demystifying CLIP Data

Reference 81

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Courville, Ruslan Salakhutdinov, Richard S

Reference 82

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Convolutional neural networks: an overview and application in radiology

Reference 83

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Observation adff5323-7e5b-4ad6-b3e1-1b5e28d8235b · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer gspan: graph-based substructure pattern mining

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.707633Z

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Hier- archical attention networks for document classification

Reference 85

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Observation 71e7d64e-c85a-40e5-b535-d432997ddf0c · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.689368Z

Source-reported events for the cited work

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Observation 80d79c7e-882d-4f43-aab9-2d0d1f38e776 · outbound

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Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:48.680026Z

Source-reported events for the cited work

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Observation d43a75c4-1d64-44d6-b699-3caad92ec17a · outbound

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Reference 88

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Reference 89

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Observation 31250797-7d71-409a-9569-2379f0e1f4d0 · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:56:48.254631Z

Source-reported events for the cited work

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Observation c05e42c6-b9e5-4565-870d-20a2fe9a01cf · outbound

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RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Graph-Bert: Only Attention is Needed for Learning Graph Representations

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:48.131771Z

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Observation f4b9ff11-785d-4355-a7af-02307cdbaa3e · outbound

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Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.670907Z

Source-reported events for the cited work

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Observation e591181f-9184-4a2a-b6d2-7bfca516e96d · outbound

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Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.643801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3f2edbd-3e69-4f9c-ac6b-b7b80ddbc3a2 · outbound

This paper cites Confirmation.

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Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.592069Z

Source-reported events for the cited work

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Observation eb272e48-9f73-4473-b1a4-e93ecba9cd0e · outbound

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Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.580686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a4868ba9-3503-4cb9-895f-c71ad2d87945 · outbound

This paper cites Confirmation.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Confirmation

Reference 108

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.570526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0f5d62e0-d497-4ff8-9752-9d020f18f5e1 · outbound

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Reference 109

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:48.662314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d1332df8-f5f4-43e0-acff-45ec6fc24703 · outbound

This paper cites open access.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer open access

Reference 110

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.654449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c071b652-1158-4e12-91e4-ac117e7cddc0 · outbound

This paper cites Created in BioRender. Zhang, J. (2024) BioRender.com/v74r180.

RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer Created in BioRender. Zhang, J. (2024) BioRender.com/v74r180

Reference 111

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:48.560860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b3e691af-4563-4c31-bdba-bfedb13a23bf · outbound

This paper cites an unresolved cited work.

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Reference 112

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:48.635392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers