Learning from Limited and Imperfect Data | Paper Citation Record

Observation 2093883e-b6b7-4f58-9208-8e328c5875fd · outbound

This paper cites Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning.

Learning from Limited and Imperfect Data Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:47.893992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91ae9940-756d-40f2-8b6f-7c0b9d0d0a09 · outbound

This paper cites Labels4free: Unsupervised seg- mentation using stylegan.

Learning from Limited and Imperfect Data Labels4free: Unsupervised seg- mentation using stylegan

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.041993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83f2a644-b095-4893-a360-d15ca788a86b · outbound

This paper cites Quantifying Attention Flow in Transformers.

Learning from Limited and Imperfect Data Quantifying Attention Flow in Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.198270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 324dcf90-39ad-4bba-a07b-2987326190b5 · outbound

This paper cites f-Domain-Adversarial Learning: Theory and Algorithms.

Learning from Limited and Imperfect Data f-Domain-Adversarial Learning: Theory and Algorithms

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:10:20.090028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 60bfece5-dc32-4115-baea-c57b925e1630 · outbound

This paper cites Degan: Data-enriching gan for retrieving representative samples from a trained classifier.

Learning from Limited and Imperfect Data Degan: Data-enriching gan for retrieving representative samples from a trained classifier

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.477459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebac7244-3df8-47ad-95d8-df2cd8b57ec2 · outbound

This paper cites One-network adversarial fairness.

Learning from Limited and Imperfect Data One-network adversarial fairness

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.670014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a56f9e5-172e-4ad3-a4da-dcecce4dfbad · outbound

This paper cites Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications.

Learning from Limited and Imperfect Data Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.854564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56320421-6c9d-4097-8e90-07ada986f6d3 · outbound

This paper cites Negative eigenvalues of the Hessian in deep neural networks.

Learning from Limited and Imperfect Data Negative eigenvalues of the Hessian in deep neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:48.969185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549e0a71-be5d-4c04-842e-adcecfbd4090 · outbound

This paper cites Hyperstyle: Stylegan inversion with hypernetworks for real image editing.

Learning from Limited and Imperfect Data Hyperstyle: Stylegan inversion with hypernetworks for real image editing

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.073449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b60dab1a-708a-445a-bf5b-45f24c34a3f0 · outbound

This paper cites The long tail: Why the future of business is selling less of more.

Learning from Limited and Imperfect Data The long tail: Why the future of business is selling less of more

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.181424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0372d278-9350-40cc-8314-78af5279625d · outbound

This paper cites Understanding sharpness-aware minimiza- tion, 2022.

Learning from Limited and Imperfect Data Understanding sharpness-aware minimiza- tion, 2022

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.412810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72041a50-cc3a-4ed0-a4b1-2edcfbb74727 · outbound

This paper cites Sharpness-Aware Minimization Leads to Low-Rank Features.

Learning from Limited and Imperfect Data Sharpness-Aware Minimization Leads to Low-Rank Features

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:10:20.062483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12321ec6-d9b9-472e-aa63-c0f851c979b1 · outbound

This paper cites Wasserstein GAN.

Learning from Limited and Imperfect Data Wasserstein GAN

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.696374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41eb67d4-96b6-4dfe-8169-8574ecd53d72 · outbound

This paper cites Theory of deep learn- ing, 2020.

Learning from Limited and Imperfect Data Theory of deep learn- ing, 2020

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.803197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73068e2a-b422-4ce0-9783-e9c2527f8e0b · outbound

This paper cites Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal.

Learning from Limited and Imperfect Data Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:49.959400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ab665f7-6e0e-4583-806e-07e05f842692 · outbound

This paper cites Schapire.

Learning from Limited and Imperfect Data Schapire

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.097305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0adc907-7896-4e8a-b148-0e44487840c8 · outbound

This paper cites Sharpness-Aware Minimization Improves Language Model Generalization.

Learning from Limited and Imperfect Data Sharpness-Aware Minimization Improves Language Model Generalization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.207703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7168f0de-4ac3-4c29-a79a-d9f901907659 · outbound

This paper cites Can we gain more from orthogonality regularizations in training deep networks? Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.

Learning from Limited and Imperfect Data Can we gain more from orthogonality regularizations in training deep networks? Advances in Neural Information Processing Systems (NeurIPS), 31, 2018

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.349920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b53588ac-0ce9-4b14-81d8-f936e52ccf04 · outbound

This paper cites VICReg: Variance-invariance-covariance regu- larization for self-supervised learning.

Learning from Limited and Imperfect Data VICReg: Variance-invariance-covariance regu- larization for self-supervised learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.463242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb979f6b-bf15-4415-8e31-175a6a05cf59 · outbound

This paper cites A theory of learning from different domains.

Learning from Limited and Imperfect Data A theory of learning from different domains

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.587065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912151fd-b943-414e-baf4-04027bef0f54 · outbound

This paper cites ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

Learning from Limited and Imperfect Data ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.675277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04c4b5fb-02d3-4153-9e5c-f34bc9594224 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.

Learning from Limited and Imperfect Data Mixmatch: A holistic approach to semi-supervised learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.774794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac615e13-0d64-4481-bc21-69c872287a99 · outbound

This paper cites AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation.

Learning from Limited and Imperfect Data AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:50.902108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec23d118-67d9-4a43-8308-a7143181455a · outbound

This paper cites Bhattacharyya.

Learning from Limited and Imperfect Data Bhattacharyya

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-06T13:10:20.020301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 21a79773-63ba-4e67-9c06-bebda8b20f96 · outbound

This paper cites Stylegan knows normal, depth, albedo, and more.

Learning from Limited and Imperfect Data Stylegan knows normal, depth, albedo, and more

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.131415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f468e7a6-6694-4cb8-9956-a7a92957fcb9 · outbound

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

Learning from Limited and Imperfect Data Experiment tracking with weights and biases, 2020

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.243095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96ad4a2a-b517-4b7e-9a6c-a1c0797aa025 · outbound

This paper cites Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape.

Learning from Limited and Imperfect Data Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:10:19.955171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a72245e-d5f4-4c1d-91fb-8acd1b576b73 · outbound

This paper cites An isoperimetric inequality on the discrete cube, and an elementary proof of the isoperimetric inequality in gauss space.

Learning from Limited and Imperfect Data An isoperimetric inequality on the discrete cube, and an elementary proof of the isoperimetric inequality in gauss space

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.440403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f9969f6-87f1-46a3-9842-1a81c5be8056 · outbound

This paper cites Finding Directions in GAN's Latent Space for Neural Face Reenactment.

Learning from Limited and Imperfect Data Finding Directions in GAN's Latent Space for Neural Face Reenactment

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.574479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e73f1cd-46e9-4db5-9d2d-9df39e4b1317 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

Learning from Limited and Imperfect Data Large scale GAN training for high fidelity natural image synthesis

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.662756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b2244c9-664d-43fc-96c3-fb5657e77c96 · outbound

This paper cites A systematic study of the class im- balance problem in convolutional neural networks.

Learning from Limited and Imperfect Data A systematic study of the class im- balance problem in convolutional neural networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.777638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 874e194c-295e-49eb-a9ea-5d721fe201ff · outbound

This paper cites What is the effect of importance weighting in deep learning? In International Conference on Machine Learning (ICML) , pages 872–881.

Learning from Limited and Imperfect Data What is the effect of importance weighting in deep learning? In International Conference on Machine Learning (ICML) , pages 872–881

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:51.954379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3020f37c-6901-40c8-8ca9-e4e7303fb8db · outbound

This paper cites Ace: Ally complementary experts for solving long-tailed recognition in one-shot.

Learning from Limited and Imperfect Data Ace: Ally complementary experts for solving long-tailed recognition in one-shot

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.077139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa405c96-bc28-4f95-8840-5fb13ef3c3c2 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Learning from Limited and Imperfect Data Learning imbalanced datasets with label-distribution-aware margin loss

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.190467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e15c6716-6798-4847-a9ba-0c238a1f8565 · outbound

This paper cites End-to-end object detection with transformers.

Learning from Limited and Imperfect Data End-to-end object detection with transformers

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.297125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffd4b1c4-3a83-498c-aed7-74f01a097185 · outbound

This paper cites Lower bounds for finding stationary points I.

Learning from Limited and Imperfect Data Lower bounds for finding stationary points I

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.439011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64dcb842-ae08-4484-ac94-37e127a13d81 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Learning from Limited and Imperfect Data Unsupervised learning of visual features by contrasting cluster assignments

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.604917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2397496-830c-434e-9617-d2be668f9dec · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Learning from Limited and Imperfect Data Emerging properties in self-supervised vision transformers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.754986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85ab8c05-8df3-4245-8535-1fcb09592cf3 · outbound

This paper cites Instance-conditioned gan.

Learning from Limited and Imperfect Data Instance-conditioned gan

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:52.868757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bdaf0ca-93fd-4ffe-926c-4a0ca830e65b · outbound

This paper cites Is facial recognition too biased to be let loose? Nature, 587(7834):347–350,.

Learning from Limited and Imperfect Data Is facial recognition too biased to be let loose? Nature, 587(7834):347–350,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.007902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbdfe252-d6d4-44fa-8ae6-82cd7b0921c1 · outbound

This paper cites SWAD: Domain Generalization by Seeking Flat Minima.

Learning from Limited and Imperfect Data SWAD: Domain Generalization by Seeking Flat Minima

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.141074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 836da3ce-8fde-40bb-8485-2ac0c65b512c · outbound

This paper cites Adaptive batch mode active learning.

Learning from Limited and Imperfect Data Adaptive batch mode active learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.232894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1074e7ee-ab4f-421e-9c12-4904061c8374 · outbound

This paper cites Semi-supervised learning (chapelle, o.

Learning from Limited and Imperfect Data Semi-supervised learning (chapelle, o

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.432674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d12d7bd6-b9dd-47d2-9d9c-578e841026b8 · outbound

This paper cites Joint transfer and batch-mode active learning.

Learning from Limited and Imperfect Data Joint transfer and batch-mode active learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.553064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa0140d-4b7f-4fce-825b-9753b51c6913 · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

Learning from Limited and Imperfect Data Entropy-sgd: Biasing gradient descent into wide valleys

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.611699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a009699c-8e09-42f1-9b06-03e4fee4ac40 · outbound

This paper cites Smote: synthetic minority over-sampling technique.

Learning from Limited and Imperfect Data Smote: synthetic minority over-sampling technique

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.683681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c60c67-a902-4ec7-9283-be9d36350c97 · outbound

This paper cites Transmix: Attend to mix for vision transformers.

Learning from Limited and Imperfect Data Transmix: Attend to mix for vision transformers

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.739882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f77bd812-da0a-4596-9e5f-e2f4ca501bbe · outbound

This paper cites Reltrans- former: A transformer-based long-tail visual relationship recognition.

Learning from Limited and Imperfect Data Reltrans- former: A transformer-based long-tail visual relationship recognition

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.875964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84341384-9a5d-4ffd-bc31-aad232e86207 · outbound

This paper cites Adversarial-learned loss for domain adaptation.

Learning from Limited and Imperfect Data Adversarial-learned loss for domain adaptation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:53.976122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51e9841e-0e90-4d10-a5ad-18b5b1574b28 · outbound

This paper cites Adversarial-Learned Loss for Domain Adaptation.

Learning from Limited and Imperfect Data Adversarial-Learned Loss for Domain Adaptation

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:10:19.926455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7976bab-e5a1-4ef0-b707-834b0b8a41fe · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Learning from Limited and Imperfect Data A Simple Framework for Contrastive Learning of Visual Representations

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.151249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f4c89bb-98de-41ad-b10d-a61cb51e2ee9 · outbound

This paper cites When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations.

Learning from Limited and Imperfect Data When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.209778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f24b88-73fd-4912-86f5-8844e1d46237 · outbound

This paper cites Domain adaptive faster r-cnn for object detection in the wild.

Learning from Limited and Imperfect Data Domain adaptive faster r-cnn for object detection in the wild

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.282492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 566a2e97-cb62-4421-8746-273e9066bd83 · outbound

This paper cites New exponential bounds and approx- imations for the computation of error probability in fading channels.

Learning from Limited and Imperfect Data New exponential bounds and approx- imations for the computation of error probability in fading channels

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.343802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd13e989-8715-46f0-8e02-15c3445e1a68 · outbound

This paper cites Smoothness and Stability in GANs.

Learning from Limited and Imperfect Data Smoothness and Stability in GANs

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.402409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a22c8819-52c6-411e-9bf9-4a5ae8fc71aa · outbound

This paper cites Improving generalization with active learning.

Learning from Limited and Imperfect Data Improving generalization with active learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.471825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b83e100-fd9a-4553-87d9-8bfe7dadf81b · outbound

This paper cites Facility location problem — Wikipedia, the free encyclopedia,.

Learning from Limited and Imperfect Data Facility location problem — Wikipedia, the free encyclopedia,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.558353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26a18234-3bbb-47e4-869a-c9cd0a5e3698 · outbound

This paper cites The cityscapes dataset for seman- tic urban scene understanding.

Learning from Limited and Imperfect Data The cityscapes dataset for seman- tic urban scene understanding

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.709016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7395397b-7f3a-46ad-8dc3-d143ff81387d · outbound

This paper cites Training well-generalizing classifiers for fairness metrics and other data-dependent constraints.

Learning from Limited and Imperfect Data Training well-generalizing classifiers for fairness metrics and other data-dependent constraints

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.777636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a25989b-87c7-44e5-bb22-410b026cc4ba · outbound

This paper cites Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals.

Learning from Limited and Imperfect Data Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.872561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09a218e6-3d79-4767-a5fc-7dcb2194e3be · outbound

This paper cites Parametric contrastive learning.

Learning from Limited and Imperfect Data Parametric contrastive learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:54.960214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d2499ac-7c87-4707-8289-bb708a4dfb2d · outbound

This paper cites Gradually vanishing bridge for adversarial domain adaptation.

Learning from Limited and Imperfect Data Gradually vanishing bridge for adversarial domain adaptation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.053430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a96a054-ba11-4015-8a50-7f546848a78b · outbound

This paper cites Class-balanced loss based on effective number of samples.

Learning from Limited and Imperfect Data Class-balanced loss based on effective number of samples

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.149064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 540a81a5-b453-4b95-bc73-eaec689f4147 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Learning from Limited and Imperfect Data Class-balanced loss based on effective number of samples

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.240933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f548b20-87de-4191-a4db-8e06bc72a6fa · outbound

This paper cites Escaping saddles with stochastic gradients.

Learning from Limited and Imperfect Data Escaping saddles with stochastic gradients

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.344903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 448b2afb-9255-4325-a064-875e9621c673 · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non- convex optimization.

Learning from Limited and Imperfect Data Identifying and attacking the saddle point problem in high-dimensional non- convex optimization

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.445553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98ca8fbe-1edd-4a56-976c-a2fe0ac962cb · outbound

This paper cites Modulating early visual processing by language.

Learning from Limited and Imperfect Data Modulating early visual processing by language

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.535337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b45ad431-38f0-467c-b242-e48ced13025a · outbound

This paper cites an unresolved cited work.

Learning from Limited and Imperfect Data Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.632027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 916ae0a1-b7c9-4803-9065-83b061caaa92 · outbound

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

Learning from Limited and Imperfect Data Imagenet: A large-scale hierarchical image database

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.738334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b6bdb31-0fef-49b7-990c-dc1b56fac14f · outbound

This paper cites Cluster alignment with a teacher for unsupervised domain adaptation.

Learning from Limited and Imperfect Data Cluster alignment with a teacher for unsupervised domain adaptation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.811826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e73f0d-846a-4f17-b88f-7d23bfe67bba · outbound

This paper cites Discriminative unsupervised feature learning with exemplar convolutional neural networks.

Learning from Limited and Imperfect Data Discriminative unsupervised feature learning with exemplar convolutional neural networks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.871539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3fc8a4b-230a-472d-812b-1492293f7a2e · outbound

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

Learning from Limited and Imperfect Data An image is worth 16x16 words: Transformers for image recognition at scale

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:55.972028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02ac5654-5fe5-4388-9ad4-40281b16d286 · outbound

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

Learning from Limited and Imperfect Data An image is worth 16x16 words: Transformers for image recognition at scale

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.051379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d1340ed-802e-4b06-a77e-383b0945dfa6 · outbound

This paper cites Global and local mixture consistency cumulative learning for long-tailed visual recognitions.

Learning from Limited and Imperfect Data Global and local mixture consistency cumulative learning for long-tailed visual recognitions

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.136573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d3a90c5-a537-43fd-844f-4455a90e2f9a · outbound

This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

Learning from Limited and Imperfect Data Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.240883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 729f58a2-32d5-4519-b212-7d4c7d1e9304 · outbound

This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Learning from Limited and Imperfect Data Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.316540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f68b364-d42c-4218-80d4-dd7a885b58bf · outbound

This paper cites Scalable learning of non-decomposable objectives.

Learning from Limited and Imperfect Data Scalable learning of non-decomposable objectives

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.376714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af0b56ad-2138-4ef7-8b99-a9983446d02d · outbound

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

Learning from Limited and Imperfect Data The pascal visual object classes (voc) challenge

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.450772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9626932b-a948-48de-9169-92891c335d06 · outbound

This paper cites Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning.

Learning from Limited and Imperfect Data Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.532696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d126fc55-cc53-4745-831c-4916cdb2054f · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Learning from Limited and Imperfect Data Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.610787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57a7dfb8-c138-4bd9-97ab-9020a31dafe7 · outbound

This paper cites Sharpness-aware mini- mization for efficiently improving generalization.

Learning from Limited and Imperfect Data Sharpness-aware mini- mization for efficiently improving generalization

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.683356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d51102f-2cfc-43e8-bbd2-a48df9f743e5 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.

Learning from Limited and Imperfect Data A decision-theoretic generalization of on-line learning and an application to boosting

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.784479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb7de7c-e935-4506-85fc-48bafff8f705 · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Learning from Limited and Imperfect Data Unsupervised domain adaptation by backpropagation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.844597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ed8243a-3764-40c9-8893-c148b54affe9 · outbound

This paper cites Domain-adversarial training of neural net- works.

Learning from Limited and Imperfect Data Domain-adversarial training of neural net- works

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.936874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37964a59-3311-4f98-8f6d-1392e4c97d3e · outbound

This paper cites Escaping from saddle points—online stochas- tic gradient for tensor decomposition.

Learning from Limited and Imperfect Data Escaping from saddle points—online stochas- tic gradient for tensor decomposition

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:56.991771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 727c1e80-d436-418f-9c97-f0acc86cde15 · outbound

This paper cites An investigation into neural net optimiza- tion via hessian eigenvalue density.

Learning from Limited and Imperfect Data An investigation into neural net optimiza- tion via hessian eigenvalue density

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.041295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e3a6376-1d32-43d5-9529-58bf52a50832 · outbound

This paper cites An investigation into neural net op- timization via hessian eigenvalue density.

Learning from Limited and Imperfect Data An investigation into neural net op- timization via hessian eigenvalue density

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.100569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3299cb5-a237-47ce-a2f4-dd24f5142834 · outbound

This paper cites A Loss Curvature Perspective on Training Instability in Deep Learning.

Learning from Limited and Imperfect Data A Loss Curvature Perspective on Training Instability in Deep Learning

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.185189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8420d55f-4461-4204-8bee-fa62af796319 · outbound

This paper cites ImageBind: One Embedding Space To Bind Them All.

Learning from Limited and Imperfect Data ImageBind: One Embedding Space To Bind Them All

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.253731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d53b4a1b-6cdb-47af-8961-5e82f78f9238 · outbound

This paper cites Satisfying real-world goals with dataset constraints.

Learning from Limited and Imperfect Data Satisfying real-world goals with dataset constraints

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.306849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f560e0b9-a765-4daa-be16-41283b66ecd8 · outbound

This paper cites Goluba and Henk A.

Learning from Limited and Imperfect Data Goluba and Henk A

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.386426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa50f320-e6eb-4236-9d01-9d96a036bc5a · outbound

This paper cites Generative adversarial nets.

Learning from Limited and Imperfect Data Generative adversarial nets

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.470534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d84aa863-eb96-4f6c-90fd-e906331bf4c2 · outbound

This paper cites Generative adversarial networks.

Learning from Limited and Imperfect Data Generative adversarial networks

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.546327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeb818e8-25d2-4356-b13c-b25b2451779a · outbound

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

Learning from Limited and Imperfect Data Bootstrap your own latent-a new approach to self-supervised learning

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.609266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63fcb08b-9dd4-4577-b546-0fbf66f59f27 · outbound

This paper cites Weighted entropy.

Learning from Limited and Imperfect Data Weighted entropy

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.707472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbcff351-882a-402b-a733-b1ae5ae3d4eb · outbound

This paper cites Improved training of wasserstein gans.

Learning from Limited and Imperfect Data Improved training of wasserstein gans

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.809084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f7acb47-f5d2-4813-ba9b-c89f72c6e649 · outbound

This paper cites Ganspace: Discovering interpretable gan controls.

Learning from Limited and Imperfect Data Ganspace: Discovering interpretable gan controls

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.871802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cf8f3f0-1ca8-43eb-b4e9-01113cb5e322 · outbound

This paper cites an unresolved cited work.

Learning from Limited and Imperfect Data Unresolved cited work

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:57.941207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e0f0f4d-5632-4261-8eb0-1307cc3c870f · outbound

This paper cites Asymmetric valleys: beyond sharp and flat local minima.

Learning from Limited and Imperfect Data Asymmetric valleys: beyond sharp and flat local minima

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:58.001360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b50b1825-5940-40de-a020-767bf4e4050a · outbound

This paper cites Deep residual learning for image recognition.

Learning from Limited and Imperfect Data Deep residual learning for image recognition

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:58.059610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers