Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education | Paper Citation Record

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This paper cites Stereoscopic universal perturba- tions across different architectures and datasets.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Stereoscopic universal perturba- tions across different architectures and datasets

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This paper cites nuscenes: A mul- timodal dataset for autonomous driving.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education nuscenes: A mul- timodal dataset for autonomous driving

Reference 2

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This paper cites 3d reprojection-driven robot navigation improves depth sens- ing.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education 3d reprojection-driven robot navigation improves depth sens- ing

Reference 3

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This paper cites Contrastive test-time adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Contrastive test-time adaptation

Reference 4

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This paper cites UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion

Reference 5

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This paper cites Cspn++: Learning context and resource aware con- volutional spatial propagation networks for depth comple- tion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Cspn++: Learning context and resource aware con- volutional spatial propagation networks for depth comple- tion

Reference 6

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This paper cites Unsupervised domain adap- tation via regularized conditional alignment.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Unsupervised domain adap- tation via regularized conditional alignment

Reference 7

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This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 8

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This paper cites Boosting adversarial at- tacks with momentum.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Boosting adversarial at- tacks with momentum

Reference 9

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This paper cites Implicit generation and mod- eling with energy-based models.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Implicit generation and mod- eling with energy-based models

Reference 10

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Observation cac0252e-1e4d-4797-8a74-990513eef9ec · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep network.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Depth map prediction from a single image using a multi-scale deep network

Reference 11

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Observation c1867bd4-584e-405a-9ae6-abef9e612f81 · outbound

This paper cites Uncertainty-aware cnns for depth completion: Uncertainty from beginning to end.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Uncertainty-aware cnns for depth completion: Uncertainty from beginning to end

Reference 12

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Observation 48853f09-fcb2-4675-8b62-eb5fe1acf93f · outbound

This paper cites All-day depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education All-day depth completion

Reference 13

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This paper cites A brief review of domain adaptation.Ad- vances in data science and information engineering: pro- ceedings from ICDATA 2020 and IKE 2020 , pages 877– 894, 2021.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A brief review of domain adaptation.Ad- vances in data science and information engineering: pro- ceedings from ICDATA 2020 and IKE 2020 , pages 877– 894, 2021

Reference 14

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This paper cites Geo- supervised visual depth prediction.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Geo- supervised visual depth prediction

Reference 15

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This paper cites Virtual worlds as proxy for multi-object tracking anal- ysis.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Virtual worlds as proxy for multi-object tracking anal- ysis

Reference 16

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This paper cites Ex- tending foundational monocular depth estimators to fish- eye cameras with calibration tokens.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Ex- tending foundational monocular depth estimators to fish- eye cameras with calibration tokens

Reference 17

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This paper cites Unsupervised do- main adaptation by backpropagation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Unsupervised do- main adaptation by backpropagation

Reference 18

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This paper cites Vision meets robotics: The kitti dataset.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Vision meets robotics: The kitti dataset

Reference 19

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This paper cites Digging into self-supervised monocular depth estimation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Digging into self-supervised monocular depth estimation

Reference 20

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This paper cites Explaining and Harnessing Adversarial Examples.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Explaining and Harnessing Adversarial Examples

Reference 21

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This paper cites Gustafsson, Martin Danelljan, Goutam Bhat, and Thomas B.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Gustafsson, Martin Danelljan, Goutam Bhat, and Thomas B

Reference 22

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This paper cites Harris and M.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Harris and M

Reference 23

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This paper cites Universal adversarial perturbations against semantic image segmentation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Universal adversarial perturbations against semantic image segmentation

Reference 24

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This paper cites Penet: Towards precise and efficient image guided depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Penet: Towards precise and efficient image guided depth completion

Reference 25

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This paper cites Adver- sarial examples are not bugs, they are features.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Adver- sarial examples are not bugs, they are features

Reference 26

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This paper cites Sparse and dense data with cnns: Depth completion and semantic segmenta- tion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sparse and dense data with cnns: Depth completion and semantic segmenta- tion

Reference 27

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This paper cites Costdcnet: Cost volume based depth completion for a single rgb-d image.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Costdcnet: Cost volume based depth completion for a single rgb-d image

Reference 28

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This paper cites Re- purposing diffusion-based image generators for monocular depth estimation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Re- purposing diffusion-based image generators for monocular depth estimation

Reference 29

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This paper cites Domain adap- tation without source data.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Domain adap- tation without source data

Reference 30

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This paper cites Sub-token vit embedding via stochastic res- onance transformers.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sub-token vit embedding via stochastic res- onance transformers

Reference 31

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This paper cites On the vi- ability of monocular depth pre-training for semantic seg- mentation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education On the vi- ability of monocular depth pre-training for semantic seg- mentation

Reference 32

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This paper cites A multi-scale guided cascade hourglass network for depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A multi-scale guided cascade hourglass network for depth completion

Reference 33

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This paper cites A comprehensive survey on source-free domain adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A comprehensive survey on source-free domain adaptation

Reference 34

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Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Model adaptation: Unsupervised domain adapta- tion without source data

Reference 35

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This paper cites A comprehensive sur- vey on test-time adaptation under distribution shifts.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A comprehensive sur- vey on test-time adaptation under distribution shifts

Reference 36

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This paper cites Statistical physics, course of theoretical physics.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Statistical physics, course of theoretical physics

Reference 37

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This paper cites Dynamic spatial propagation network for depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Dynamic spatial propagation network for depth completion

Reference 38

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This paper cites Monitored distillation for positive congruent depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Monitored distillation for positive congruent depth completion

Reference 39

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This paper cites TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? In Advances in Neural Information Pro- cessing Systems , pages 21808–21820.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? In Advances in Neural Information Pro- cessing Systems , pages 21808–21820

Reference 40

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This paper cites Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data

Reference 41

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This paper cites Self-supervised sparse-to-dense: Self- supervised depth completion from lidar and monocular camera.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Self-supervised sparse-to-dense: Self- supervised depth completion from lidar and monocular camera

Reference 42

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 43

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This paper cites SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 44

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Deepfool: a simple and accurate method to fool deep neural networks

Reference 45

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This paper cites Generalizable data-free objective for crafting uni- versal adversarial perturbations.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Generalizable data-free objective for crafting uni- versal adversarial perturbations

Reference 46

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This paper cites Cross-domain transferability of adversarial perturbations.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Cross-domain transferability of adversarial perturbations

Reference 47

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

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Indoor segmentation and support inference from rgbd images

Reference 48

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Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Unresolved cited work

Reference 49

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This paper cites Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Reference 50

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This paper cites Efficient Test-Time Model Adaptation without Forgetting.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Efficient Test-Time Model Adaptation without Forgetting

Reference 51

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This paper cites Test- time adaptation for depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Test- time adaptation for depth completion

Reference 52

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This paper cites Non-local spatial propagation network for depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Non-local spatial propagation network for depth completion

Reference 53

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This paper cites Lower bounds on the robustness to adversarial per- turbations.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Lower bounds on the robustness to adversarial per- turbations

Reference 54

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This paper cites Moment matching for multi-source domain adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Moment matching for multi-source domain adaptation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.417318Z

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Observation dced8db0-0177-4f5d-9609-821f05c9b326 · outbound

This paper cites Deepli- dar: Deep surface normal guided depth prediction for out- door scene from sparse lidar data and single color image.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Deepli- dar: Deep surface normal guided depth prediction for out- door scene from sparse lidar data and single color image

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.408039Z

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Observation 96f75c0c-173c-4561-b7f5-2c77d8856b97 · outbound

This paper cites Depth comple- tion via deep basis fitting.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Depth comple- tion via deep basis fitting

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.398146Z

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Observation 841264f0-0159-4109-9e09-2f9e054afda7 · outbound

This paper cites Bayesian deep basis fitting for depth completion with uncertainty.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Bayesian deep basis fitting for depth completion with uncertainty

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.388612Z

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Observation e0e215af-d042-444b-a55a-dc85fc4c7082 · outbound

This paper cites Attacking optical flow.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Attacking optical flow

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.378765Z

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Observation 96b25cfc-9359-488e-bc8c-88bd9fe845da · outbound

This paper cites Guide- former: Transformers for image guided depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Guide- former: Transformers for image guided depth completion

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.369698Z

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Observation 5916fdcd-d163-46f8-8368-d4ea794ae149 · outbound

This paper cites Radar-Guided Polynomial Fitting for Metric Depth Estimation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Radar-Guided Polynomial Fitting for Metric Depth Estimation

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:06:34.951145Z

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Observation 78c0ca61-a782-480f-a4e7-482f563efd49 · outbound

This paper cites Protodepth: Unsupervised con- tinual depth completion with prototypes.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Protodepth: Unsupervised con- tinual depth completion with prototypes

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.360667Z

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Observation 6ed4f3b1-c4b1-4def-8012-86aca068a2c3 · outbound

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

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education High-resolution image synthesis with latent diffusion models

Reference 63

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Observation 41dd6f2e-c4da-48ba-8945-4fab3de08fd7 · outbound

This paper cites Learning depth from single monocular images.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Learning depth from single monocular images

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.345604Z

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Observation 5e806659-1a1f-45af-8e53-eca37612d0e4 · outbound

This paper cites Improving robustness against common corruptions by covariate shift adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Improving robustness against common corruptions by covariate shift adaptation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T23:06:34.537189Z

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Observation 73d10b5d-cd20-4180-9fd9-e34d93e24e07 · outbound

This paper cites Towards Understanding Adversarial Robustness of Optical Flow Networks.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Towards Understanding Adversarial Robustness of Optical Flow Networks

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:06:34.927832Z

Source-reported events for the cited work

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Observation 25a902ca-b82c-4169-aa50-e794d79a5fe3 · outbound

This paper cites Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.336866Z

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Observation 519d663c-05ad-4e55-977a-88604a618c57 · outbound

This paper cites Dfusenet: Deep fusion of rgb and sparse depth information for image guided dense depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Dfusenet: Deep fusion of rgb and sparse depth information for image guided dense depth completion

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.328226Z

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Observation e1d8ec11-78b4-41f4-91f0-f80b430e3cb7 · outbound

This paper cites Depth estimation from camera image and mmwave radar point cloud.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Depth estimation from camera image and mmwave radar point cloud

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.319694Z

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Observation e3a00319-9acc-4bf2-9cc7-2c966e54e974 · outbound

This paper cites Sliced score matching: A scalable approach to density and score estimation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sliced score matching: A scalable approach to density and score estimation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.310460Z

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Observation 40dff42f-b22e-4b5e-866f-de331e3650ad · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Scalability in perception for autonomous driving: Waymo open dataset

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.301744Z

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Observation 5f0d8a74-d2e9-4b8a-8b75-f1fee277b0db · outbound

This paper cites Test-Time Training with Self- Supervision for Generalization under Distribution Shifts.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Test-Time Training with Self- Supervision for Generalization under Distribution Shifts

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.292458Z

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Observation f5b33c71-3075-45fc-824b-cc9dcf583a56 · outbound

This paper cites Intriguing properties of neural networks.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Intriguing properties of neural networks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T23:06:34.562537Z

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Observation aa94e745-2dc4-4506-a255-63bc5a129429 · outbound

This paper cites Bilateral propagation network for depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Bilateral propagation network for depth completion

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.283307Z

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Observation 580596e8-1b4c-49ec-8478-384e7ed224a7 · outbound

This paper cites Sparsity in- variant cnns.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sparsity in- variant cnns

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.274282Z

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Observation 47b87810-6e06-43f4-872a-d914229e23f9 · outbound

This paper cites Enhancing diffusion models with 3d perspec- tive geometry constraints.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Enhancing diffusion models with 3d perspec- tive geometry constraints

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.264789Z

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Observation 162d9ff6-2177-4bd3-b853-86aaf540aa8e · outbound

This paper cites Pixel recurrent neural networks.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Pixel recurrent neural networks

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.256112Z

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Observation 4929cd37-05c4-4819-89eb-242f24cf8c9a · outbound

This paper cites Sparse and noisy lidar completion with rgb guidance and uncertainty.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sparse and noisy lidar completion with rgb guidance and uncertainty

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.247110Z

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Observation b025a4be-242e-41d9-9eb5-7d48f52a85b8 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A connection between score matching and denoising autoencoders

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.238197Z

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Observation 856df9cc-19ad-415f-8dfa-902339597759 · outbound

This paper cites Tent: Fully test-time adapta- tion by entropy minimization.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Tent: Fully test-time adapta- tion by entropy minimization

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.229430Z

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Observation 6f81eaeb-8623-4b2d-a2c2-336f0736887d · outbound

This paper cites Continual test-time domain adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Continual test-time domain adaptation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.220532Z

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Observation a8360731-be64-4b49-9ab1-404b8c1be7eb · outbound

This paper cites A survey of unsupervised deep domain adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education A survey of unsupervised deep domain adaptation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.211635Z

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Observation d27619c6-576f-4f3f-87aa-aeaa741c167a · outbound

This paper cites Bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.202736Z

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Observation d439f741-cb18-41f5-b949-281ae283828d · outbound

This paper cites Unsupervised depth com- pletion with calibrated backprojection layers.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Unsupervised depth com- pletion with calibrated backprojection layers

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.192741Z

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Observation 57ccedc8-868e-42c2-9fa1-c755991b5b43 · outbound

This paper cites Targeted ad- versarial perturbations for monocular depth prediction.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Targeted ad- versarial perturbations for monocular depth prediction

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.183373Z

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Observation 0cdaefd1-20d4-4a26-b465-fed6af12244e · outbound

This paper cites Unsupervised depth completion from visual iner- tial odometry.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Unsupervised depth completion from visual iner- tial odometry

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.174309Z

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Observation 190af3da-12f1-464c-8d27-09c72ec07818 · outbound

This paper cites Learning topology from synthetic data for unsupervised depth com- pletion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Learning topology from synthetic data for unsupervised depth com- pletion

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.165345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe04bbee-dd9f-4139-a141-f0125a9faba0 · outbound

This paper cites An adaptive framework for learning unsupervised depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education An adaptive framework for learning unsupervised depth completion

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.155321Z

Source-reported events for the cited work

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Observation 8bf3a9e2-897c-46bf-b939-1ce4e224e33e · outbound

This paper cites Stere- opagnosia: Fooling stereo networks with adversarial pertur- bations.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Stere- opagnosia: Fooling stereo networks with adversarial pertur- bations

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.146536Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64abb166-8713-455c-94a4-f3f5a86dcc48 · outbound

This paper cites Augundo: Scaling up augmentations for monocular depth completion and estima- tion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Augundo: Scaling up augmentations for monocular depth completion and estima- tion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.137770Z

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Observation 6590ca2d-90fd-4560-a3ec-06e98ced6691 · outbound

This paper cites Quadric representations for lidar odometry, mapping and localization.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Quadric representations for lidar odometry, mapping and localization

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.128664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 29136f94-8b84-474e-ab90-bd57815d28f0 · outbound

This paper cites Adversarial examples for se- mantic segmentation and object detection.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Adversarial examples for se- mantic segmentation and object detection

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.119563Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2db4c0f-69bd-4f8d-b681-ee2e26580a2a · outbound

This paper cites Improving transfer- ability of adversarial examples with input diversity.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Improving transfer- ability of adversarial examples with input diversity

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.110626Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a6c2edd-81a3-47be-a70b-1cd62c498dc5 · outbound

This paper cites Sparsefusion: Fusing multi- modal sparse representations for multi-sensor 3d object detection.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Sparsefusion: Fusing multi- modal sparse representations for multi-sensor 3d object detection

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.101228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf56db83-0dd5-4d93-8551-66bea548a6bb · outbound

This paper cites Depth completion from sparse lidar data with depth-normal constraints.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Depth completion from sparse lidar data with depth-normal constraints

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.091703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d097c5c3-75b1-4c4a-8283-4cf2ffb77ff7 · outbound

This paper cites Desnet: Decomposed scale-consistent network for unsupervised depth completion.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Desnet: Decomposed scale-consistent network for unsupervised depth completion

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.082244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cee6dad4-d313-49db-a11e-7c8dba5b07be · outbound

This paper cites Dense depth posterior (ddp) from single image and sparse range.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Dense depth posterior (ddp) from single image and sparse range

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.072990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4731300b-3905-4bf7-8e2c-b463c9ee482d · outbound

This paper cites Rapid network adaptation: Learning to adapt neural networks using test-time feedback.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Rapid network adaptation: Learning to adapt neural networks using test-time feedback

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.063383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36ba4428-3e6c-4bd6-87b9-c30a898013e7 · outbound

This paper cites CompletionFormer: Depth Completion with Convolutions and Vision Transformers.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education CompletionFormer: Depth Completion with Convolutions and Vision Transformers

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:06:34.904384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 030b4608-b36c-4d6a-910f-51fd17d53ab7 · outbound

This paper cites Tea: Test-time energy adaptation.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education Tea: Test-time energy adaptation

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:35.053708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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