DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers | Paper Citation Record

Observation 9eefebe5-f3e8-4a64-abe7-eadeca7d4602 · outbound

This paper cites Rep- resentation alignment for generation: Training diffusion transformers is easier than you think,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Rep- resentation alignment for generation: Training diffusion transformers is easier than you think,

Reference 1

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Observation 0c3606d6-35e8-43c3-8c95-8fe4987939d9 · outbound

This paper cites Diffuse and Disperse: Image Generation with Representation Regularization.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffuse and Disperse: Image Generation with Representation Regularization

Reference 2

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Observation 5c8996ab-23e1-4270-9de0-bc151040a849 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 3

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Observation 52697dd3-a854-4bc1-8e6b-1193f90ae653 · outbound

This paper cites Denoising diffusion probabilistic models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Denoising diffusion probabilistic models,

Reference 4

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This paper cites Scalable diffusion models with transformers,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Scalable diffusion models with transformers,

Reference 5

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Observation ddc0f6bf-d64b-47e4-a42e-e2c72dad1b36 · outbound

This paper cites All are worth words: A vit backbone for diffusion models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers All are worth words: A vit backbone for diffusion models,

Reference 6

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This paper cites Sana: Efficient high-resolution text-to-image syn- thesis with linear diffusion transformers,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Sana: Efficient high-resolution text-to-image syn- thesis with linear diffusion transformers,

Reference 7

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Observation c28791e2-3a2b-4166-9e1a-49c2050f2762 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers High- resolution image synthesis with latent diffusion models,

Reference 8

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Observation 0dfca87a-b90f-4f8d-80bb-5ac014aad640 · outbound

This paper cites Cogvideox: Text-to-video diffusion models with an expert transformer,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Cogvideox: Text-to-video diffusion models with an expert transformer,

Reference 9

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Observation 5d2e2771-8153-4826-91a3-b1b56047e00f · outbound

This paper cites Photorealistic video generation with diffusion models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Photorealistic video generation with diffusion models,

Reference 10

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Observation 39d51981-72b7-4c3e-9c04-affec5a06a92 · outbound

This paper cites Diffusion based representation learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffusion based representation learning,

Reference 11

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Observation 134f6868-af80-41ee-931c-311937d7df17 · outbound

This paper cites Deconstructing denoising diffusion models for self-supervised learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Deconstructing denoising diffusion models for self-supervised learning,

Reference 12

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Observation dfeb2d4c-e52e-43f3-8a34-9d569ae32008 · outbound

This paper cites Denoising diffusion autoencoders are unified self-supervised learners,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Denoising diffusion autoencoders are unified self-supervised learners,

Reference 13

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Observation 6c7ca48a-ffe0-4408-9d27-8222aecc8508 · outbound

This paper cites REPA-E: Unlocking vae for end-to-end tuning with latent diffusion transformers,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers REPA-E: Unlocking vae for end-to-end tuning with latent diffusion transformers,

Reference 14

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Observation 00d560bd-210f-4e98-a581-f411d87bc61b · outbound

This paper cites Representation entanglement for generation: Training diffusion transformers is much easier than you think,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Representation entanglement for generation: Training diffusion transformers is much easier than you think,

Reference 15

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Observation 182eee78-7985-4f0c-99b8-94008af7ee9a · outbound

This paper cites No other representation component is needed: Diffusion transformers can provide representation guidance by themselves,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers No other representation component is needed: Diffusion transformers can provide representation guidance by themselves,

Reference 16

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Observation a13f6251-2b14-48b0-aba9-530e7f87c20b · outbound

This paper cites Similarity of neural network representations revisited,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Similarity of neural network representations revisited,

Reference 17

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Observation d2e436a3-fea7-4f5b-9ef3-a60022f4ba23 · outbound

This paper cites Mean flows for one- step generative modeling,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Mean flows for one- step generative modeling,

Reference 18

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Observation dce06210-f13b-445c-996d-ba23df1e4ea0 · outbound

This paper cites Diversedit: Towards diverse representation learning in diffusion transformers,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diversedit: Towards diverse representation learning in diffusion transformers,

Reference 19

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Observation aa854ff2-394b-418c-bfa3-71900c02d30d · outbound

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

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Representation learning: A review and new perspectives,

Reference 20

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Observation b2620a47-e57f-41ad-a772-f0a5a4e3d689 · outbound

This paper cites Disentangled rep- resentation learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Disentangled rep- resentation learning,

Reference 21

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Observation 4dfa7185-fcbd-4837-8fa0-1617ea2b6ec4 · outbound

This paper cites Deep diversity- enhanced feature representation of hyperspectral images,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Deep diversity- enhanced feature representation of hyperspectral images,

Reference 22

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Observation d16ef599-840f-4dcc-a1cc-f21bef074fe0 · outbound

This paper cites Self-supervised visual feature learning with deep neural networks: A survey,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Self-supervised visual feature learning with deep neural networks: A survey,

Reference 23

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Observation 97fc1321-3aa8-4a65-8780-89cda0add52c · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Bootstrap your own latent-a new approach to self-supervised learning,

Reference 24

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Observation 045253d3-f742-4e1e-a781-0083d5d47eed · outbound

This paper cites Dinov2: Learning robust visual features without supervision,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Dinov2: Learning robust visual features without supervision,

Reference 25

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Observation f5e6173b-ea95-4242-8fa1-1da41529aff6 · outbound

This paper cites An empirical study of training self- supervised vision transformers,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers An empirical study of training self- supervised vision transformers,

Reference 26

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Auto-encoding variational bayes,

Reference 27

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Observation b615f2e8-4ce1-4bd3-aa6f-64ff005a5201 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Masked autoencoders are scalable vision learners,

Reference 28

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This paper cites Simmim: A simple framework for masked image modeling,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Simmim: A simple framework for masked image modeling,

Reference 29

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Observation c2f6e436-f2c7-4428-80aa-d911f8dd1002 · outbound

This paper cites Diffusion model as representation learner,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffusion model as representation learner,

Reference 30

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Observation 820ecf71-70ac-46e4-8ab9-a338593886e9 · outbound

This paper cites Unsupervised representation learning from pre-trained diffusion probabilistic models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Unsupervised representation learning from pre-trained diffusion probabilistic models,

Reference 31

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Observation 64dfffef-6fe6-483c-a4ff-3eb9d8508720 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Learning transferable visual models from natural language supervision,

Reference 32

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 33

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This paper cites Sigmoid loss for language image pre-training,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Sigmoid loss for language image pre-training,

Reference 34

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Observation 0a74da7e-a838-4e7d-90eb-1d97549d86e1 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 35

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers ImageNet: A large-scale hierarchical image database,

Reference 36

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Observation 8703410c-28a3-4db6-87e0-b950e9513f3f · outbound

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Assessing generative models via precision and recall,

Reference 37

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Im- proved precision and recall metric for assessing generative models,

Reference 38

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This paper cites Reliability of cka as a similarity measure in deep learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Reliability of cka as a similarity measure in deep learning,

Reference 39

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 40

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Observation ac5e7456-eff6-4fc3-9b75-214a98fa1b55 · outbound

This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models,

Reference 41

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Generating images with sparse representations,

Reference 42

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Observation 136afa9d-5d3c-48f3-82d3-420633237ba4 · outbound

This paper cites Denoising diffusion implicit models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Denoising diffusion implicit models,

Reference 43

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Observation 61814b8b-dc8c-471d-a5f1-e78f87021968 · outbound

This paper cites Diffusion models in vision: A survey,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffusion models in vision: A survey,

Reference 44

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This paper cites Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis,

Reference 45

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This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 46

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Observation 61e9b3e3-67db-4f82-bf2d-17a73de12051 · outbound

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Flow matching for generative modeling,

Reference 47

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Flow straight and fast: Learning to generate and transfer data with rectified flow,

Reference 48

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This paper cites Diffusion models beat gans on image synthesis,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffusion models beat gans on image synthesis,

Reference 49

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Observation 43d895ed-75b8-4200-8b2a-7fc497534bea · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 50

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DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers SiT: Exploring flow and diffusion-based generative models with scalable interpolant transformers,

Reference 51

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Observation 44bcbdd8-fe9d-4ad4-9146-68cc19ab9595 · outbound

This paper cites Dreamteacher: Pretraining image backbones with deep generative models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Dreamteacher: Pretraining image backbones with deep generative models,

Reference 52

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This paper cites Diffusion models and representation learning: A survey,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Diffusion models and representation learning: A survey,

Reference 53

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Observation fc4ad0a0-471d-4bea-a2b0-4bfc0d587da1 · outbound

This paper cites SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models

Reference 54

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Observation 08bfffde-d632-4c85-b63d-1a75b20dc617 · outbound

This paper cites Aligning text to image in diffusion models is easier than you think,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Aligning text to image in diffusion models is easier than you think,

Reference 55

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Observation f980f830-eb3f-4f76-9951-a0e3e5ffdd28 · outbound

This paper cites Learning diffusion models with flexible representation guidance,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Learning diffusion models with flexible representation guidance,

Reference 56

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Observation 6af938d4-c781-4599-83a2-5f13a29641cf · outbound

This paper cites What matters for representation alignment: Global information or spatial structure?.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers What matters for representation alignment: Global information or spatial structure?

Reference 57

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Source-reported events for the cited work

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Observation 32c32a77-a467-439e-bfba-8aaa1c4ddfe4 · outbound

This paper cites Generative flows on discrete state-spaces: enabling multimodal flows with applications to protein co-design,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Generative flows on discrete state-spaces: enabling multimodal flows with applications to protein co-design,

Reference 58

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Observation aabccb85-3476-4fdf-9922-0995e0497758 · outbound

This paper cites Ro- bust deep learning–based protein sequence design using proteinmpnn,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Ro- bust deep learning–based protein sequence design using proteinmpnn,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:19.184733Z

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Observation 11d7d31f-1d1d-4df3-b7af-1bb3516aa3eb · outbound

This paper cites MiDi: Mixed graph and 3d denoising diffusion for molecule generation,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers MiDi: Mixed graph and 3d denoising diffusion for molecule generation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:19.044752Z

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Observation a9e81d4d-55c4-468d-810e-a5502143546e · outbound

This paper cites Navigating the design space of equivariant diffusion-based generative models for de novo 3d molecule generation,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Navigating the design space of equivariant diffusion-based generative models for de novo 3d molecule generation,

Reference 61

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Observation ff7431ac-8849-44f8-b3f0-5a26904dfc7e · outbound

This paper cites SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching

Reference 62

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Observation b9bccc26-39ed-4697-81eb-ea51ad6b02bc · outbound

This paper cites Accurate structure prediction of biomolecular interactions with AlphaFold 3,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Accurate structure prediction of biomolecular interactions with AlphaFold 3,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:18.714751Z

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Observation 02c3c8e0-99d9-4631-9cab-f2083ae1807e · outbound

This paper cites Uni-mol: A universal 3d molecular representation learning framework,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Uni-mol: A universal 3d molecular representation learning framework,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:18.554739Z

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Observation 50ff010f-6e7c-4c55-8ce7-3a3f43d2aaa0 · outbound

This paper cites Measuring statistical dependence with hilbert-schmidt norms,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Measuring statistical dependence with hilbert-schmidt norms,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:18.404738Z

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Observation bc09b4bf-f238-4a22-ab58-f3958c660956 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Masked autoencoders are scalable vision learners,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:18.255945Z

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Observation e7ca1dc6-5518-4125-b080-dd5d081c1401 · outbound

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

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Imagenet: A large-scale hierarchical image database,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:18.117941Z

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This paper cites Improved techniques for training gans,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Improved techniques for training gans,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.986698Z

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Observation d1eee231-7846-4424-ad9a-f9e09e377b9f · outbound

This paper cites Classifier-Free Diffusion Guidance.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Classifier-Free Diffusion Guidance

Reference 69

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Source-reported events for the cited work

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Observation 6e9ca393-77d6-456a-b9a3-42739ece78cd · outbound

This paper cites Understanding diffusion objectives as the elbo with simple data augmentation,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Understanding diffusion objectives as the elbo with simple data augmentation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.814773Z

Source-reported events for the cited work

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Observation 070fbff6-e2bf-4b7a-9271-360dd458fe47 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Cascaded diffusion models for high fidelity image generation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.614743Z

Source-reported events for the cited work

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Observation c1c96284-a72f-4c44-b406-4281586cd685 · outbound

This paper cites Sd- dit: Unleashing the power of self-supervised discrimination in diffusion transformer,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Sd- dit: Unleashing the power of self-supervised discrimination in diffusion transformer,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.444739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51831a14-7900-4cd2-95da-7d1eb6f19e1f · outbound

This paper cites Fast Training of Diffusion Models with Masked Transformers.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Fast Training of Diffusion Models with Masked Transformers

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:11.974745Z

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Observation 9cad9d96-40dd-4e63-920c-63489b70685c · outbound

This paper cites MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:12.008720Z

Source-reported events for the cited work

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Observation d4a9e613-a062-4272-9861-37d5bbeac011 · outbound

This paper cites Improved techniques for training consistency models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Improved techniques for training consistency models,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.274753Z

Source-reported events for the cited work

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Observation cbff9c18-fa9f-4c0b-848e-11d6308fb232 · outbound

This paper cites One step diffusion via shortcut models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers One step diffusion via shortcut models,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:17.124756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14e727ac-74af-4882-85c5-c01b9c5ac2f6 · outbound

This paper cites Inductive moment matching,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Inductive moment matching,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.993642Z

Source-reported events for the cited work

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Observation a9e5a9fa-c527-4811-8b76-a2a4122f859d · outbound

This paper cites Fine-tuning discrete diffusion models via reward optimization with applications to dna and protein design,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Fine-tuning discrete diffusion models via reward optimization with applications to dna and protein design,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.852501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae672168-234c-4205-b02c-324d239b27ca · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Evolutionary-scale prediction of atomic-level protein structure with a language model,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.664776Z

Source-reported events for the cited work

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Observation 10890b9d-2310-4ed7-8e11-d3236f9972a2 · outbound

This paper cites GEOM, energy-annotated molecular conformations for property prediction and molecular genera- tion,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers GEOM, energy-annotated molecular conformations for property prediction and molecular genera- tion,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.544764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27c457d6-3c5f-4d48-890e-0319c40eb4d3 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Quantum chemistry structures and properties of 134 kilo molecules,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.374762Z

Source-reported events for the cited work

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Observation b17bd7fa-2365-4ddf-b18a-1042c16dcffc · outbound

This paper cites The effective rank: A measure of effective dimensionality,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers The effective rank: A measure of effective dimensionality,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.206741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7aef3941-e08d-47c2-aa7c-1da82381b31b · outbound

This paper cites Visualizing and understanding convolu- tional networks,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Visualizing and understanding convolu- tional networks,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:16.079223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0815330-f90f-4575-85c1-22442a96095a · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:12.475484Z

Source-reported events for the cited work

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Observation e2b44cc8-b3b3-4a69-8f19-f2297294924a · outbound

This paper cites Decoupled Weight Decay Regularization.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Decoupled Weight Decay Regularization

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:12.504753Z

Source-reported events for the cited work

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Observation 1d3a38bd-8e8c-4fcc-9c24-4ee085baa1fc · outbound

This paper cites Return of unconditional generation: A self- supervised representation generation method,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Return of unconditional generation: A self- supervised representation generation method,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:15.934755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65f2cd6f-3a54-42bc-827e-bc94ecdfe500 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Momentum contrast for unsupervised visual representation learning,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:15.765775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 43dcd4b5-e794-44e7-b260-a4fde22e7df3 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Improved Baselines with Momentum Contrastive Learning

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:12.658352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30ffb1e3-d539-40b8-a591-e5559fa428f4 · outbound

This paper cites Rethinking the inception architecture for computer vision,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Rethinking the inception architecture for computer vision,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T01:07:15.604752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b32ea848-d074-4bd9-9950-2ff780e351d8 · outbound

This paper cites Improved denoising diffusion probabilis- tic models,.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Improved denoising diffusion probabilis- tic models,

Reference 90

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T01:07:15.455042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers