DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations | Paper Citation Record

Observation b37506f3-d9f3-4597-98f9-86ccb16cfcb7 · outbound

This paper cites Mtlora: Low-rank adaptation approach for effi- cient multi-task learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Mtlora: Low-rank adaptation approach for effi- cient multi-task learning

Reference 1

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Observation 6e533ece-7edc-43a5-9eaa-0d6e99d48832 · outbound

This paper cites Construction of bayesian deformable mod- els via a stochastic approximation algorithm: a con- vergence study.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Construction of bayesian deformable mod- els via a stochastic approximation algorithm: a con- vergence study

Reference 2

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Observation f1bf178f-8d35-4cae-b125-66dc35d61b1f · outbound

This paper cites Bayesian mixed effect atlas esti- mation with a diffeomorphic deformation model.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Bayesian mixed effect atlas esti- mation with a diffeomorphic deformation model

Reference 3

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Observation ed2548a4-bc9b-4d9e-a84a-f51fef8757b9 · outbound

This paper cites Experiment tracking with weights and biases, 2020.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Experiment tracking with weights and biases, 2020

Reference 4

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Observation 74faaba8-41dc-424f-801c-f927e8fccfaa · outbound

This paper cites Transformers learn through gradual rank increase.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Transformers learn through gradual rank increase

Reference 5

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Observation 9e027113-339f-4996-9b6a-447f5516f564 · outbound

This paper cites What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?

Reference 6

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This paper cites Multitask learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Multitask learning

Reference 7

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Observation d8f029d4-597d-4e85-b258-a2ad9ef223c7 · outbound

This paper cites Trainable highly-expressive activa- tion functions.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Trainable highly-expressive activa- tion functions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.518491Z

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Observation 09220aab-c07a-4d08-a551-c0acd688b92c · outbound

This paper cites Class-Balanced Loss Based on Effective Number of Samples.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Class-Balanced Loss Based on Effective Number of Samples

Reference 9

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Observation fb84db04-ac39-49bc-acdb-6ea93dfa1eea · outbound

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

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Imagenet: A large-scale hierar- chical image database

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.502653Z

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Observation f59a6f92-3dc1-4610-873c-596d6d7ababc · outbound

This paper cites Deep diffeomorphic transformer net- works.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Deep diffeomorphic transformer net- works

Reference 11

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Observation 729a4e89-51b5-463d-b0c9-0b43ba1bba8e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.473876Z

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Observation de01b52e-93e6-42b0-82ad-00ae9cf218fc · outbound

This paper cites The pascal visual object classes (voc) challenge.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations The pascal visual object classes (voc) challenge

Reference 13

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Observation a55e63ac-c0b4-4c2b-be46-9d05e89f0382 · outbound

This paper cites Highly-expressive spaces of well-behaved transformations: Keeping it simple.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Highly-expressive spaces of well-behaved transformations: Keeping it simple

Reference 14

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Observation 5178ee3e-921d-4fbd-b057-b51f1e58a9dd · outbound

This paper cites Transformations based on continuous piecewise-affine velocity fields.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Transformations based on continuous piecewise-affine velocity fields

Reference 15

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Observation 41dcd886-5c8e-49df-837f-5a78bf86a5be · outbound

This paper cites Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 16

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Observation 1c15031e-35fe-429c-bb89-170461db5388 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 17

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Observation 3dfc71c7-1293-4fd1-a81d-cafa846a16e4 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Parameter-Efficient Transfer Learning for NLP

Reference 18

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Observation 4425bbf9-f008-47d1-a2e9-b7e0a534c00b · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations LoRA: Low-rank adaptation of large language models

Reference 19

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Observation 3251b07c-666e-487b-ba64-6be8ed135b9d · outbound

This paper cites Hospedales.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Hospedales

Reference 20

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Observation 9d779879-47c3-44c5-b0f3-f0f3b5ff15cd · outbound

This paper cites Going beyond multi-task dense prediction with synergy embedding models.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Going beyond multi-task dense prediction with synergy embedding models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.394544Z

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Observation c9827610-9de7-48ac-957d-5800ba2e69f7 · outbound

This paper cites Visual prompt tuning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Visual prompt tuning

Reference 22

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Observation f09996d9-18b4-43b4-921d-6ea6ffe5612b · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Compacter: Efficient low-rank hypercomplex adapter layers

Reference 23

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Observation fa34d3eb-36a1-4083-9f06-ace5ea5b199c · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.343722Z

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Observation 3fb7a2fb-1520-4ae9-82ae-70df88290dca · outbound

This paper cites Svft: Parameter- efficient fine-tuning with singular vectors, 2024.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Svft: Parameter- efficient fine-tuning with singular vectors, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.327555Z

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Observation 0b022950-bab9-4aed-81e3-017c1490d09a · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 26

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Observation d6f10306-1863-4aae-be2a-aa502d219140 · outbound

This paper cites Polyhistor: Parameter-efficient multi-task adaptation for dense vision tasks.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Polyhistor: Parameter-efficient multi-task adaptation for dense vision tasks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.312261Z

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Observation a1140358-3ddf-4dd3-87b4-da14ea812b28 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Swin transformer: Hierarchical vision transformer using shifted windows

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.295850Z

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Observation 0899479c-cc29-4227-b003-75ae6f913138 · outbound

This paper cites Decoupled Weight Decay Regularization.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Decoupled Weight Decay Regularization

Reference 29

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Observation 55f51b3b-bfad-4c17-b5d1-826d8bf5be39 · outbound

This paper cites Investigating Forgetting in Pre-Trained Representations Through Continual Learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Investigating Forgetting in Pre-Trained Representations Through Continual Learning

Reference 30

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Observation e6d2683e-f09b-43e0-9a29-476f51bef298 · outbound

This paper cites Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

Reference 31

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Observation 20079d84-b34a-4deb-82d0-01269ab3137a · outbound

This paper cites Rethink- ing fine-tuning through geometric perspective.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Rethink- ing fine-tuning through geometric perspective

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.279795Z

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Observation aef3dcf3-d2e9-48af-b238-b88f9542f491 · outbound

This paper cites DiGRAF: Dif- feomorphic graph-adaptive activation function.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations DiGRAF: Dif- feomorphic graph-adaptive activation function

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.263206Z

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Observation a795a58f-370e-4012-8286-13e212ad4a61 · outbound

This paper cites Closed-form diffeomorphic transforma- tions for time series alignment.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Closed-form diffeomorphic transforma- tions for time series alignment

Reference 34

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Observation 6ae095bb-2127-475b-b820-1b4b0b5af29d · outbound

This paper cites Cross-stitch networks for multi- task learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Cross-stitch networks for multi- task learning

Reference 35

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Observation 9cf12cc1-062e-406a-a113-9c56406cf5e3 · outbound

This paper cites Insights on representational similarity in neural networks with canonical correlation.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Insights on representational similarity in neural networks with canonical correlation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.216211Z

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Observation a32758a8-caaf-4fab-9de3-e0c1a517840c · outbound

This paper cites Pre-trained vision and language trans- formers are few-shot incremental learners.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Pre-trained vision and language trans- formers are few-shot incremental learners

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.200497Z

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Observation 13763a61-9477-4a9f-b2d3-deb8c2df5efd · outbound

This paper cites Language mod- els are unsupervised multitask learners.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Language mod- els are unsupervised multitask learners

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.180137Z

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Observation 119bc70d-bba8-4878-a5a4-27bbe3bfb46e · outbound

This paper cites Imagenet large scale visual recog- nition challenge.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Imagenet large scale visual recog- nition challenge

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.162958Z

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Observation f79e7243-f45c-4c20-9dfc-a27bb3aac3ec · outbound

This paper cites Saxe, James L.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Saxe, James L

Reference 40

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Observation 6daf6521-a093-4654-88bb-9b3922999ce4 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Indoor segmentation and support inference from rgbd images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.132935Z

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Observation 2ff296f9-cc39-425d-85f6-cb943fced636 · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 42

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Observation 966a40f3-a17f-4ff5-b357-df0896cfdee4 · outbound

This paper cites Varia- tional pdes for acceleration on manifolds and appli- cation to diffeomorphisms.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Varia- tional pdes for acceleration on manifolds and appli- cation to diffeomorphisms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.115689Z

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Observation ff29d1b4-869a-4d72-b987-772a7a63f988 · outbound

This paper cites Vl- adapter: Parameter-efficient transfer learning for vision-and-language tasks.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Vl- adapter: Parameter-efficient transfer learning for vision-and-language tasks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.099440Z

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Observation 894997bd-68ed-41bb-8538-9b7fec6f64fd · outbound

This paper cites Is learning the n-th thing any easier than learning the first? In D.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Is learning the n-th thing any easier than learning the first? In D

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.084515Z

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Observation 551c4561-6486-4d71-83ad-b729d9149c16 · outbound

This paper cites Mti-net: Multi-scale task interac- tion networks for multi-task learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Mti-net: Multi-scale task interac- tion networks for multi-task learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.070480Z

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Observation 89a198b2-3529-4d4a-9d00-8991bfcffcf0 · outbound

This paper cites Multi-task learning for dense pre- diction tasks: A survey.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Multi-task learning for dense pre- diction tasks: A survey

Reference 47

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Observation ff1b17a5-6a06-43a3-8fcb-3a63e9dbff27 · outbound

This paper cites Attention is all you need.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Attention is all you need

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.055766Z

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Observation d629c8ad-d350-4aaf-b250-b68970135e2f · outbound

This paper cites Continuous Piecewise-Affine Based Motion Model for Image Animation.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Continuous Piecewise-Affine Based Motion Model for Image Animation

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:03:50.551714Z

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Observation c5f75f10-272a-418e-8ded-89643e2b077e · outbound

This paper cites Deep high-resolution representation learning for vi- sual recognition.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Deep high-resolution representation learning for vi- sual recognition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.038727Z

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Observation 2b582bea-ffea-4a8e-8517-e37c29631066 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.023077Z

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Observation 03964537-e22e-4c2c-9a96-d4966e21b076 · outbound

This paper cites Regularization-free diffeomorphic temporal alignment nets.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Regularization-free diffeomorphic temporal alignment nets

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:51.006474Z

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Observation 55a09c00-fef4-4e4e-9f8a-8f0d3a06d894 · outbound

This paper cites ReFT: Representation Finetuning for Language Models.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations ReFT: Representation Finetuning for Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T17:03:50.273157Z

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Unavailable: canonical work link unavailable.

Observation c1335968-be8b-4ab5-abc9-a6d91f6ebc77 · outbound

This paper cites Pad-net: Multi-tasks guided prediction-and- distillation network for simultaneous depth estima- tion and scene parsing.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Pad-net: Multi-tasks guided prediction-and- distillation network for simultaneous depth estima- tion and scene parsing

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.990454Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec4605da-5434-46ac-8eb9-fbf6fabf3bd9 · outbound

This paper cites Inverted pyramid multi- task transformer for dense scene understanding.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Inverted pyramid multi- task transformer for dense scene understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.974501Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 444efded-22e4-41f5-84af-94f05943e30d · outbound

This paper cites Gradient surgery for multi-task learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Gradient surgery for multi-task learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.957934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00fa43a3-6480-48f1-a3a7-ecc3e5949bc9 · outbound

This paper cites Gradient surgery for multi-task learning.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Gradient surgery for multi-task learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.941718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4f50d9e4-dda1-4cae-bfa3-7e1599ce3cbb · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T17:03:50.295974Z

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Observation da4cccc9-f698-4567-a175-6fe43d0831f9 · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T17:03:50.300281Z

Source-reported events for the cited work

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Observation 4b4580d3-9a9d-4170-959a-3fdbf3b26f9a · outbound

This paper cites Bayesian statistical shape analysis on the manifold of diffeo- morphisms.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Bayesian statistical shape analysis on the manifold of diffeo- morphisms

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.925389Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7ed739b7-69bf-463b-b5de-91c1cb07d753 · outbound

This paper cites W = UΣV⊤, where: U ∈ Rc2×c2 , Σ = diag(σ1,.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations W = UΣV⊤, where: U ∈ Rc2×c2 , Σ = diag(σ1,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.905847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a51f2c6c-981a-4ca5-9630-e853ce96e527 · outbound

This paper cites an unresolved cited work.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:03:50.887986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ccd78dba-081c-49be-a056-e6ccd06ff350 · outbound

This paper cites an unresolved cited work.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Unresolved cited work

Reference 64

Resolution: parse uncertain; raw_fallback, observed 2026-08-08T17:03:50.869680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 669bc76d-5ef5-4a64-b8fa-2293a71202c3 · outbound

This paper cites an unresolved cited work.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:03:50.853531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 59410593-fde5-4fa6-ab79-28c30ff2247b · outbound

This paper cites xk = x if not last block, else xk B.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations xk = x if not last block, else xk B

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.837933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 49221d63-1709-4b3d-be84-49c9beebc5cd · outbound

This paper cites Gradient Analysis We analyze the memory requirements for low-rank adap- tation methods, such as LoRA, and compare them with DITASK in terms of gradient storage.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Gradient Analysis We analyze the memory requirements for low-rank adap- tation methods, such as LoRA, and compare them with DITASK in terms of gradient storage

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:03:50.820653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fdc426eb-7f4f-4421-baca-7de26efb96c2 · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-08T17:03:49.990728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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