Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation | Paper Citation Record

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This paper cites Learning representations and generative models for 3D point clouds.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Learning representations and generative models for 3D point clouds

Reference 1

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This paper cites Efficient string matching: an aid to bibliographic search.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Efficient string matching: an aid to bibliographic search

Reference 2

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This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Neural Machine Translation by Jointly Learning to Align and Translate

Reference 3

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This paper cites Neural machine translation by jointly learning to align and translate, 2016.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Neural machine translation by jointly learning to align and translate, 2016

Reference 4

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This paper cites Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields

Reference 5

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This paper cites Viewpoint textual inversion: Discovering scene representa- tions and 3d view control in 2d diffusion models, 2024.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Viewpoint textual inversion: Discovering scene representa- tions and 3d view control in 2d diffusion models, 2024

Reference 6

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This paper cites Efficient-vqgan: To- wards high-resolution image generation with efficient vision transformers.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Efficient-vqgan: To- wards high-resolution image generation with efficient vision transformers

Reference 7

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This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation ShapeNet: An Information-Rich 3D Model Repository

Reference 8

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This paper cites PixelSNAIL: An improved autoregressive genera- tive model.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation PixelSNAIL: An improved autoregressive genera- tive model

Reference 9

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Observation 8cdaac1f-e3b0-4ad3-b209-8f0f81174458 · outbound

This paper cites Up- gpt: Universal diffusion model for person image generation, editing and pose transfer, 2023.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Up- gpt: Universal diffusion model for person image generation, editing and pose transfer, 2023

Reference 10

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This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 11

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This paper cites The road less scheduled, 2024.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation The road less scheduled, 2024

Reference 12

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This paper cites T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings

Reference 13

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This paper cites Imagenet: A large-scale hierarchical image database.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Imagenet: A large-scale hierarchical image database

Reference 14

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This paper cites The mnist database of handwritten digit images for machine learning research.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation The mnist database of handwritten digit images for machine learning research

Reference 15

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Observation 24e8d92d-7c77-4c62-923f-ba7d7aa763ae · outbound

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

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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Observation 5c04605d-0c88-4d36-84f6-2f893496a985 · outbound

This paper cites The Llama 3 Herd of Models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation The Llama 3 Herd of Models

Reference 17

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Observation aba82095-5abb-4069-b7ac-6fbd8c14d956 · outbound

This paper cites Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data

Reference 18

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Observation bbaa8e96-9bf4-4016-9746-e1790a49c5d4 · outbound

This paper cites Taming Transformers for High-Resolution Image Synthesis.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Taming Transformers for High-Resolution Image Synthesis

Reference 19

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This paper cites Ccedit: Creative and controllable video editing via diffu- sion models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Ccedit: Creative and controllable video editing via diffu- sion models

Reference 20

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This paper cites Generative adversarial nets.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Generative adversarial nets

Reference 21

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Observation 7d5a5619-821d-4d4c-9d74-76be55e020f8 · outbound

This paper cites Vec- tor quantized diffusion model for text-to-image synthesis.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Vec- tor quantized diffusion model for text-to-image synthesis

Reference 22

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This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 23

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This paper cites Reducing the dimensionality of data with neural networks.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Reducing the dimensionality of data with neural networks

Reference 24

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This paper cites Denoising dif- fusion probabilistic models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Denoising dif- fusion probabilistic models

Reference 25

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This paper cites Towards accurate image coding: Improved au- toregressive image generation with dynamic vector quantiza- tion (supplementary material).

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Towards accurate image coding: Improved au- toregressive image generation with dynamic vector quantiza- tion (supplementary material)

Reference 26

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This paper cites Octree trans- former: Autoregressive 3d shape generation on hierarchi- cally structured sequences.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Octree trans- former: Autoregressive 3d shape generation on hierarchi- cally structured sequences

Reference 27

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This paper cites EdiBERT, a generative model for image editing.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation EdiBERT, a generative model for image editing

Reference 28

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This paper cites One-shot learning meets depth diffusion in multi-object videos, 2024.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation One-shot learning meets depth diffusion in multi-object videos, 2024

Reference 29

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This paper cites Scaling Laws for Neural Language Models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Scaling Laws for Neural Language Models

Reference 30

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This paper cites LookupViT: Compressing visual information to a limited number of tokens.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation LookupViT: Compressing visual information to a limited number of tokens

Reference 31

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This paper cites Learning multiple layers of features from tiny images.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Learning multiple layers of features from tiny images

Reference 32

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This paper cites Sentencepiece: A sim- ple and language independent subword tokenizer and detok- enizer for neural text processing, 2018.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Sentencepiece: A sim- ple and language independent subword tokenizer and detok- enizer for neural text processing, 2018

Reference 33

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This paper cites Openassistant conversa- tions – democratizing large language model alignment, 2023.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Openassistant conversa- tions – democratizing large language model alignment, 2023

Reference 34

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This paper cites Robust training of vector quantized bottleneck models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Robust training of vector quantized bottleneck models

Reference 35

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Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Autoregressive image generation using residual quantization

Reference 36

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Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Flow Matching for Generative Modeling

Reference 37

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This paper cites Deep learning face attributes in the wild.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Deep learning face attributes in the wild

Reference 38

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This paper cites Decoupled weight decay regularization, 2019.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Decoupled weight decay regularization, 2019

Reference 39

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This paper cites Reading digits in natural images with unsupervised feature learning.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Reading digits in natural images with unsupervised feature learning

Reference 40

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This paper cites Gpt-4 technical report,.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Gpt-4 technical report,

Reference 41

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This paper cites Tokenisation and sentence segmentation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Tokenisation and sentence segmentation

Reference 42

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Observation fec46ec3-b2b9-475c-ab2e-56903c590644 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Movie Gen: A Cast of Media Foundation Models

Reference 43

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Observation daf3a746-00a5-4d71-8179-9fa9adce4bbe · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 44

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This paper cites Improving language understanding with unsuper- vised learning.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Improving language understanding with unsuper- vised learning

Reference 45

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Observation 91fc3779-2a2c-4474-ac1e-1031b3e31d97 · outbound

This paper cites Language models are unsu- pervised multitask learners.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Language models are unsu- pervised multitask learners

Reference 46

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This paper cites Zero-Shot Text-to-Image Generation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Zero-Shot Text-to-Image Generation

Reference 47

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This paper cites A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces

Reference 48

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Observation 1395ce5d-c3d2-4cbb-bc2d-5bda18942590 · outbound

This paper cites Gener- ating diverse high-fidelity images with vq-vae-2, 2019.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Gener- ating diverse high-fidelity images with vq-vae-2, 2019

Reference 49

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Observation 54bcb473-87b0-4548-8355-ac2feb1b5910 · outbound

This paper cites Pixel-level BPE for auto-regressive image generation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Pixel-level BPE for auto-regressive image generation

Reference 50

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Observation 61a89d1c-3db0-437a-bd9a-eb30d8d34110 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 51

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Observation 59439e50-7832-4fa0-9641-1b632efa1e3d · outbound

This paper cites Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed

Reference 52

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Observation 7a2982ee-ee3c-46e8-a1c9-e9251f89ebf3 · outbound

This paper cites Learning factorial codes by pre- dictability minimization.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Learning factorial codes by pre- dictability minimization

Reference 53

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Observation 714bdddd-960e-4683-a272-810a815423f9 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Neural Machine Translation of Rare Words with Subword Units

Reference 54

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This paper cites Bivdiff: A training-free framework for general-purpose video synthesis via bridging image and video diffusion models, 2024.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Bivdiff: A training-free framework for general-purpose video synthesis via bridging image and video diffusion models, 2024

Reference 55

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This paper cites Byte pair encoding: A text compression scheme that accelerates pattern matching.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Byte pair encoding: A text compression scheme that accelerates pattern matching

Reference 56

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This paper cites Fast WordPiece Tokenization.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Fast WordPiece Tokenization

Reference 57

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This paper cites Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech

Reference 58

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This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 59

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Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Going Deeper with Convolutions

Reference 60

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Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Neural Discrete Representation Learning

Reference 61

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This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 62

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Observation b92d2b4b-1247-4ac4-ac90-bc06bfdc1202 · outbound

This paper cites Vector quantized wasserstein auto-encoder, 2023.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Vector quantized wasserstein auto-encoder, 2023

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:55:34.575084Z

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This paper cites MambaByte: Token-free Selective State Space Model.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation MambaByte: Token-free Selective State Space Model

Reference 64

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Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Vector quantization-based regularization for autoencoders

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:55:34.558188Z

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This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 66

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This paper cites An Image is Worth 32 Tokens for Reconstruction and Generation.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation An Image is Worth 32 Tokens for Reconstruction and Generation

Reference 67

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This paper cites Online clustered code- book.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Online clustered code- book

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:55:34.541979Z

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Observation 913398fe-5673-4c4a-930b-5d49ff4f8cc2 · outbound

This paper cites Designing a Better Asymmetric VQGAN for StableDiffusion.

Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation Designing a Better Asymmetric VQGAN for StableDiffusion

Reference 69

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