Deep Loss Convexification for Learning Iterative Models | Paper Citation Record

Observation bf03b438-2235-4708-b39f-f82c5bf59cd2 · outbound

This paper cites A comprehensive survey on point cloud registration.

Deep Loss Convexification for Learning Iterative Models A comprehensive survey on point cloud registration

Reference 1

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This paper cites Least-squares fitting of two 3-d point sets,.

Deep Loss Convexification for Learning Iterative Models Least-squares fitting of two 3-d point sets,

Reference 2

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This paper cites Prnet: Self-supervised learning for partial- to-partial registration,.

Deep Loss Convexification for Learning Iterative Models Prnet: Self-supervised learning for partial- to-partial registration,

Reference 3

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This paper cites an unresolved cited work.

Deep Loss Convexification for Learning Iterative Models Unresolved cited work

Reference 4

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This paper cites A survey of optimization methods from a machine learning perspective,.

Deep Loss Convexification for Learning Iterative Models A survey of optimization methods from a machine learning perspective,

Reference 5

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Observation 5fd7cef2-188b-4d7e-9ade-b9e2321b4255 · outbound

This paper cites Recent Theoretical Advances in Non-Convex Optimization.

Deep Loss Convexification for Learning Iterative Models Recent Theoretical Advances in Non-Convex Optimization

Reference 6

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This paper cites The power of convex relaxation: Near-optimal matrix completion,.

Deep Loss Convexification for Learning Iterative Models The power of convex relaxation: Near-optimal matrix completion,

Reference 7

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This paper cites Non-convex optimization for machine learning,.

Deep Loss Convexification for Learning Iterative Models Non-convex optimization for machine learning,

Reference 8

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This paper cites Exact matrix completion via convex opti- mization,.

Deep Loss Convexification for Learning Iterative Models Exact matrix completion via convex opti- mization,

Reference 9

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Observation 6f9af533-69bc-4000-a1f6-d434f9dacb4d · outbound

This paper cites An alternative view: When does sgd escape local minima?.

Deep Loss Convexification for Learning Iterative Models An alternative view: When does sgd escape local minima?

Reference 10

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This paper cites Visualizing the loss landscape of neural nets,.

Deep Loss Convexification for Learning Iterative Models Visualizing the loss landscape of neural nets,

Reference 11

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Observation f36413c2-99f2-4cb1-a872-3c333b5dbabd · outbound

This paper cites SGD converges to global minimum in deep learning via star-convex path,.

Deep Loss Convexification for Learning Iterative Models SGD converges to global minimum in deep learning via star-convex path,

Reference 12

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This paper cites Near-optimal methods for min- imizing star-convex functions and beyond,.

Deep Loss Convexification for Learning Iterative Models Near-optimal methods for min- imizing star-convex functions and beyond,

Reference 13

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Observation ed32c27d-6a20-4893-a39f-b213d2a7386f · outbound

This paper cites Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks.

Deep Loss Convexification for Learning Iterative Models Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks

Reference 14

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Observation 29243b95-c66b-42ba-9b2b-2cad744e2bfc · outbound

This paper cites Cubic regularization of newton method and its global performance,.

Deep Loss Convexification for Learning Iterative Models Cubic regularization of newton method and its global performance,

Reference 15

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Observation 8b3b0b58-6e49-4e49-848a-c826f794e170 · outbound

This paper cites Optimizing star-convex functions,.

Deep Loss Convexification for Learning Iterative Models Optimizing star-convex functions,

Reference 16

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Observation cd5a1c78-fb88-47f0-b75e-91eba87001f8 · outbound

This paper cites Near-optimal methods for minimizing star-convex functions and beyond,.

Deep Loss Convexification for Learning Iterative Models Near-optimal methods for minimizing star-convex functions and beyond,

Reference 17

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This paper cites Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation,.

Deep Loss Convexification for Learning Iterative Models Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation,

Reference 18

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Observation 3e317dd3-bb88-4661-985f-b432dde7bd6b · outbound

This paper cites Sequential subspace optimization for quasar-convex optimization problems with inexact gradient,.

Deep Loss Convexification for Learning Iterative Models Sequential subspace optimization for quasar-convex optimization problems with inexact gradient,

Reference 19

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This paper cites Prise: Demystifying deep lucas- kanade with strongly star-convex constraints for multimodel image alignment,.

Deep Loss Convexification for Learning Iterative Models Prise: Demystifying deep lucas- kanade with strongly star-convex constraints for multimodel image alignment,

Reference 20

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Observation 2fc78480-38e6-4538-aca8-1225c8bfe3b9 · outbound

This paper cites Adversarial weight perturbation helps robust generalization,.

Deep Loss Convexification for Learning Iterative Models Adversarial weight perturbation helps robust generalization,

Reference 21

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Observation 998774f4-6b1d-4fb8-9de4-65d5d422abf5 · outbound

This paper cites LossPlot: A Better Way to Visualize Loss Landscapes.

Deep Loss Convexification for Learning Iterative Models LossPlot: A Better Way to Visualize Loss Landscapes

Reference 22

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Observation 2ac440c7-7f62-4ab7-bf67-45782f0580e9 · outbound

This paper cites Deep Ensembles: A Loss Landscape Perspective.

Deep Loss Convexification for Learning Iterative Models Deep Ensembles: A Loss Landscape Perspective

Reference 23

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This paper cites Exploring the landscape of spatial robustness,.

Deep Loss Convexification for Learning Iterative Models Exploring the landscape of spatial robustness,

Reference 24

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Observation 745fd17a-a398-4671-b6dd-1169524ae645 · outbound

This paper cites The loss landscape of overparameterized neural networks.

Deep Loss Convexification for Learning Iterative Models The loss landscape of overparameterized neural networks

Reference 25

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This paper cites Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances,.

Deep Loss Convexification for Learning Iterative Models Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances,

Reference 26

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This paper cites Embedding principle of loss landscape of deep neural networks,.

Deep Loss Convexification for Learning Iterative Models Embedding principle of loss landscape of deep neural networks,

Reference 27

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This paper cites The global landscape of neural networks: An overview,.

Deep Loss Convexification for Learning Iterative Models The global landscape of neural networks: An overview,

Reference 28

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This paper cites Low nonconvexity-rank bilinear matrix inequalities: algorithms and applications in robust controller and struc- ture designs,.

Deep Loss Convexification for Learning Iterative Models Low nonconvexity-rank bilinear matrix inequalities: algorithms and applications in robust controller and struc- ture designs,

Reference 29

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Observation 60261775-68b9-483b-ac41-f5671572fb15 · outbound

This paper cites Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima.

Deep Loss Convexification for Learning Iterative Models Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima

Reference 30

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Observation e1e5f899-c443-4d95-804d-6cb7cda0ff35 · outbound

This paper cites Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima,.

Deep Loss Convexification for Learning Iterative Models Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima,

Reference 31

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Observation 6f4555a0-6c07-4b95-8960-0afecf8935e4 · outbound

This paper cites Graduated non- convexity for robust spatial perception: From non-minimal solvers to global outlier rejection,.

Deep Loss Convexification for Learning Iterative Models Graduated non- convexity for robust spatial perception: From non-minimal solvers to global outlier rejection,

Reference 32

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Observation 7ee423d7-97f9-4be1-9a69-f185a22156d3 · outbound

This paper cites Adaptively Solving the Local-Minimum Problem for Deep Neural Networks.

Deep Loss Convexification for Learning Iterative Models Adaptively Solving the Local-Minimum Problem for Deep Neural Networks

Reference 33

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Observation 31dc430b-a234-4e01-a1b9-e8fea8156255 · outbound

This paper cites Successive convexification of non-convex optimal control problems and its convergence properties,.

Deep Loss Convexification for Learning Iterative Models Successive convexification of non-convex optimal control problems and its convergence properties,

Reference 34

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This paper cites Adaptive meth- ods for nonconvex optimization,.

Deep Loss Convexification for Learning Iterative Models Adaptive meth- ods for nonconvex optimization,

Reference 35

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This paper cites Regularized deep learning with nonconvex penalties.

Deep Loss Convexification for Learning Iterative Models Regularized deep learning with nonconvex penalties

Reference 36

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This paper cites Learning a similarity metric discriminatively, with application to face verification,.

Deep Loss Convexification for Learning Iterative Models Learning a similarity metric discriminatively, with application to face verification,

Reference 37

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This paper cites Dimensionality reduction by learning an invariant mapping,.

Deep Loss Convexification for Learning Iterative Models Dimensionality reduction by learning an invariant mapping,

Reference 38

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This paper cites Representation Learning with Contrastive Predictive Coding.

Deep Loss Convexification for Learning Iterative Models Representation Learning with Contrastive Predictive Coding

Reference 39

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This paper cites Contrastive multiview coding,.

Deep Loss Convexification for Learning Iterative Models Contrastive multiview coding,

Reference 40

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This paper cites A simple framework for contrastive learning of visual representations,.

Deep Loss Convexification for Learning Iterative Models A simple framework for contrastive learning of visual representations,

Reference 41

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

Deep Loss Convexification for Learning Iterative Models Momentum contrast for unsupervised visual representation learning,

Reference 42

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This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

Deep Loss Convexification for Learning Iterative Models Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 43

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This paper cites Understanding the behaviour of contrastive loss,.

Deep Loss Convexification for Learning Iterative Models Understanding the behaviour of contrastive loss,

Reference 44

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This paper cites Contrastive learning inverts the data generating process,.

Deep Loss Convexification for Learning Iterative Models Contrastive learning inverts the data generating process,

Reference 45

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This paper cites Contrastive boundary learning for point cloud segmentation,.

Deep Loss Convexification for Learning Iterative Models Contrastive boundary learning for point cloud segmentation,

Reference 46

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This paper cites Unsupervised point cloud object co-segmentation by co-contrastive learning and mutual attention sampling,.

Deep Loss Convexification for Learning Iterative Models Unsupervised point cloud object co-segmentation by co-contrastive learning and mutual attention sampling,

Reference 47

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This paper cites Contrastive representation learning: A framework and review,.

Deep Loss Convexification for Learning Iterative Models Contrastive representation learning: A framework and review,

Reference 48

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This paper cites Omnet: Learning overlapping mask for partial-to-partial point cloud registration,.

Deep Loss Convexification for Learning Iterative Models Omnet: Learning overlapping mask for partial-to-partial point cloud registration,

Reference 49

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This paper cites Pointnetlk revisited,.

Deep Loss Convexification for Learning Iterative Models Pointnetlk revisited,

Reference 50

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This paper cites Geometric trans- former for fast and robust point cloud registration,.

Deep Loss Convexification for Learning Iterative Models Geometric trans- former for fast and robust point cloud registration,

Reference 51

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This paper cites Regtr: End-to-end point cloud correspon- dences with transformers,.

Deep Loss Convexification for Learning Iterative Models Regtr: End-to-end point cloud correspon- dences with transformers,

Reference 52

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This paper cites Efficient sparse icp,.

Deep Loss Convexification for Learning Iterative Models Efficient sparse icp,

Reference 53

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This paper cites Outlier robust icp for minimizing fractional rmsd,.

Deep Loss Convexification for Learning Iterative Models Outlier robust icp for minimizing fractional rmsd,

Reference 54

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Observation 20c0468e-7f0d-4f54-94b8-6d0c64521fdc · outbound

This paper cites A new point matching algorithm for non- rigid registration,.

Deep Loss Convexification for Learning Iterative Models A new point matching algorithm for non- rigid registration,

Reference 55

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Observation 4c14a5c0-3076-4165-a9d9-043cc9147b01 · outbound

This paper cites A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates.

Deep Loss Convexification for Learning Iterative Models A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates

Reference 56

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Observation 6bbc4e33-8f0a-4694-b097-dbe0cd6366be · outbound

This paper cites Teaser: Fast and certifiable point cloud registration,.

Deep Loss Convexification for Learning Iterative Models Teaser: Fast and certifiable point cloud registration,

Reference 57

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Observation f89088dd-527b-4074-b1b6-08711123dc6a · outbound

This paper cites Pointnetlk: Robust & efficient point cloud registration using pointnet,.

Deep Loss Convexification for Learning Iterative Models Pointnetlk: Robust & efficient point cloud registration using pointnet,

Reference 58

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Observation 5c1d8fa4-ed1d-475e-93c8-600e63090422 · outbound

This paper cites Deep closest point: Learning represen- tations for point cloud registration,.

Deep Loss Convexification for Learning Iterative Models Deep closest point: Learning represen- tations for point cloud registration,

Reference 59

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Observation d0c4c6c9-5251-4c2d-850a-4ada54d21a37 · outbound

This paper cites PCRNet: Point Cloud Registration Network using PointNet Encoding.

Deep Loss Convexification for Learning Iterative Models PCRNet: Point Cloud Registration Network using PointNet Encoding

Reference 60

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Observation d94aa56a-9311-463c-a78d-0e9689a5ccda · outbound

This paper cites Rpm-net: Robust point matching using learned features,.

Deep Loss Convexification for Learning Iterative Models Rpm-net: Robust point matching using learned features,

Reference 61

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Observation 8b9d0ba0-14eb-46f1-bb7e-e7439baa8ae2 · outbound

This paper cites Deep global registration,.

Deep Loss Convexification for Learning Iterative Models Deep global registration,

Reference 62

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4f7b2538-d958-44d8-9096-b73817ee5596 · outbound

This paper cites Predator: Registration of 3d point clouds with low overlap,.

Deep Loss Convexification for Learning Iterative Models Predator: Registration of 3d point clouds with low overlap,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.285422Z

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Observation 456db996-908a-4f50-bd67-8c744d4b656a · outbound

This paper cites Deep learning based point cloud registra- tion: an overview,.

Deep Loss Convexification for Learning Iterative Models Deep learning based point cloud registra- tion: an overview,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.269099Z

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Observation 96f417d6-b610-46a2-93e3-123787ccfc70 · outbound

This paper cites Robust registration of multimodal remote sensing images based on structural similarity,.

Deep Loss Convexification for Learning Iterative Models Robust registration of multimodal remote sensing images based on structural similarity,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.251353Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 277ab462-2a3a-47ab-bf74-aec7c926a98c · outbound

This paper cites Adaptive context network for scene parsing,.

Deep Loss Convexification for Learning Iterative Models Adaptive context network for scene parsing,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.236113Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0fcad4ed-9356-4db8-b54c-284387b351b1 · outbound

This paper cites Fast and robust matching for multimodal remote sensing image registration,.

Deep Loss Convexification for Learning Iterative Models Fast and robust matching for multimodal remote sensing image registration,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.220735Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd86a1f5-73d7-4ad9-89b2-c3c7d7242e73 · outbound

This paper cites Homography estimation from image pairs with hierarchical convolutional networks,.

Deep Loss Convexification for Learning Iterative Models Homography estimation from image pairs with hierarchical convolutional networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.204774Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5c6f252b-30f4-4498-997f-556b7ffd4a62 · outbound

This paper cites Unsupervised deep homography: A fast and robust homography esti- mation model,.

Deep Loss Convexification for Learning Iterative Models Unsupervised deep homography: A fast and robust homography esti- mation model,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.188572Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ac240b02-b27d-4430-b35d-5d7c0a3bf280 · outbound

This paper cites Deep homography estima- tion for dynamic scenes,.

Deep Loss Convexification for Learning Iterative Models Deep homography estima- tion for dynamic scenes,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.171679Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation db4f7f4b-633c-4db2-bcea-fdc053a07957 · outbound

This paper cites Content-aware unsupervised deep homography estimation,.

Deep Loss Convexification for Learning Iterative Models Content-aware unsupervised deep homography estimation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.153735Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e4862497-dd9d-44ca-bede-d679656f2cbf · outbound

This paper cites Deep Image Homography Estimation.

Deep Loss Convexification for Learning Iterative Models Deep Image Homography Estimation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.543617Z

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Observation 1e378ee0-8f25-4170-9138-b84016e6773b · outbound

This paper cites Clkn: Cascaded lucas- kanade networks for image alignment,.

Deep Loss Convexification for Learning Iterative Models Clkn: Cascaded lucas- kanade networks for image alignment,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.136133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 50eafd13-5a83-432d-8080-087c3454b27e · outbound

This paper cites Deep lucas-kanade homography for multimodal image alignment,.

Deep Loss Convexification for Learning Iterative Models Deep lucas-kanade homography for multimodal image alignment,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.118372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29423ea6-fa38-4b82-9ea9-dee7cc265f87 · outbound

This paper cites Iterative deep homography estimation,.

Deep Loss Convexification for Learning Iterative Models Iterative deep homography estimation,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.102310Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f38ed67-256c-475f-9238-f42ac8c7c711 · outbound

This paper cites A survey of planar homog- raphy estimation techniques,.

Deep Loss Convexification for Learning Iterative Models A survey of planar homog- raphy estimation techniques,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.086237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d1dbd1e-d013-410a-b49e-e775f16b1398 · outbound

This paper cites Deep neural networks on diffeomorphism groups for optimal shape reparameterization.

Deep Loss Convexification for Learning Iterative Models Deep neural networks on diffeomorphism groups for optimal shape reparameterization

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:32:11.195136Z

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Observation cbe0c4f8-9753-43fd-a331-57f42e6974ee · outbound

This paper cites Deep reparametrization of multi-frame super-resolution and denoising,.

Deep Loss Convexification for Learning Iterative Models Deep reparametrization of multi-frame super-resolution and denoising,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.069653Z

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Observation 1185f861-e6dd-44a7-8ea6-ffb18a0c39a6 · outbound

This paper cites An iterative image registration technique with an application to stereo vision,.

Deep Loss Convexification for Learning Iterative Models An iterative image registration technique with an application to stereo vision,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.053480Z

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Observation 20658dd6-32db-41bc-a845-05daa4fb295f · outbound

This paper cites an unresolved cited work.

Deep Loss Convexification for Learning Iterative Models Unresolved cited work

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.587347Z

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Observation c8d2108e-eecc-4416-9ca0-0c3f0d2bdf27 · outbound

This paper cites Convergence analysis of two-layer neural networks with relu activation,.

Deep Loss Convexification for Learning Iterative Models Convergence analysis of two-layer neural networks with relu activation,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.027826Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 78eb7b1d-9e3e-4c0d-816a-4421f6bf4741 · outbound

This paper cites Smpconv: Self-moving point representations for continuous convolution,.

Deep Loss Convexification for Learning Iterative Models Smpconv: Self-moving point representations for continuous convolution,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:12.012056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d725957-d611-4b60-910a-6272ed85db09 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Deep Loss Convexification for Learning Iterative Models Efficiently Modeling Long Sequences with Structured State Spaces

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.602422Z

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Observation 7bddb3a0-31a0-4aac-a639-ab15550b13d0 · outbound

This paper cites FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes.

Deep Loss Convexification for Learning Iterative Models FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:32:11.145428Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a670ed8-7944-44f4-bef0-eb4094b3c5fe · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers,.

Deep Loss Convexification for Learning Iterative Models Combining recurrent, convolutional, and continuous-time models with linear state space layers,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.612888Z

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Observation ed847549-d00e-459e-9621-8d2e9d2d1b20 · outbound

This paper cites Long Expressive Memory for Sequence Modeling.

Deep Loss Convexification for Learning Iterative Models Long Expressive Memory for Sequence Modeling

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.618913Z

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Observation a2c41712-2271-4a82-8187-d248438337e4 · outbound

This paper cites Deep Independently Recurrent Neural Network (IndRNN).

Deep Loss Convexification for Learning Iterative Models Deep Independently Recurrent Neural Network (IndRNN)

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:32:11.100104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ec1f5863-5bc9-44aa-bba6-ef6718ba4ab3 · outbound

This paper cites Coupled oscillatory recurrent neural network (cornn): An accurate and (gradient) stable architecture for learning long time dependencies,.

Deep Loss Convexification for Learning Iterative Models Coupled oscillatory recurrent neural network (cornn): An accurate and (gradient) stable architecture for learning long time dependencies,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:11.982356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a4a4a58b-f86b-41f3-a620-9d4547c9b573 · outbound

This paper cites Lipschitz Recurrent Neural Networks.

Deep Loss Convexification for Learning Iterative Models Lipschitz Recurrent Neural Networks

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.634787Z

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Unavailable: canonical work link unavailable.

Observation 2fe03589-d8a7-44b1-ae09-15b013aca0da · outbound

This paper cites Gating revisited: Deep multi-layer rnns that can be trained,.

Deep Loss Convexification for Learning Iterative Models Gating revisited: Deep multi-layer rnns that can be trained,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:11.966353Z

Source-reported events for the cited work

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Observation faff8c90-2d5a-4553-b9f8-252104d62a18 · outbound

This paper cites CKConv: Continuous Kernel Convolution For Sequential Data.

Deep Loss Convexification for Learning Iterative Models CKConv: Continuous Kernel Convolution For Sequential Data

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.650530Z

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Observation 26176e12-8734-4c2b-906f-072462c3589a · outbound

This paper cites Recurrent Batch Normalization.

Deep Loss Convexification for Learning Iterative Models Recurrent Batch Normalization

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.659389Z

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Observation 4fcd6293-1fd1-4c21-b9a8-6c5eec386b85 · outbound

This paper cites Unitary evolution recurrent neural networks,.

Deep Loss Convexification for Learning Iterative Models Unitary evolution recurrent neural networks,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:11.949669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9d6fde05-2458-4203-92b4-be349966a340 · outbound

This paper cites Long short-term memory,.

Deep Loss Convexification for Learning Iterative Models Long short-term memory,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.672719Z

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Observation 6599214c-3825-4d11-bc53-95affce02de5 · outbound

This paper cites MNIST handwritten digit database,.

Deep Loss Convexification for Learning Iterative Models MNIST handwritten digit database,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.677465Z

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Unavailable: canonical work link unavailable.

Observation 17dcf888-a962-4993-b1f2-ca2c53f86dec · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes,.

Deep Loss Convexification for Learning Iterative Models 3d shapenets: A deep representation for volumetric shapes,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:11.912427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f87d749-4f29-4f7b-bf51-d5eedb87120a · outbound

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

Deep Loss Convexification for Learning Iterative Models ShapeNet: An Information-Rich 3D Model Repository

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.689228Z

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Unavailable: canonical work link unavailable.

Observation c8808dea-30c1-40ac-80ca-cdf9426d977c · outbound

This paper cites 3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions,.

Deep Loss Convexification for Learning Iterative Models 3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.695100Z

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Observation ea89f881-cc9e-4bed-9fcc-f4aa33904c3c · outbound

This paper cites Fully convolutional geometric fea- tures,.

Deep Loss Convexification for Learning Iterative Models Fully convolutional geometric fea- tures,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:11.883709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 376729d0-4e58-4f15-a11e-9a3ed004d9bf · outbound

This paper cites D3feat: Joint learning of dense detection and description of 3d local features,.

Deep Loss Convexification for Learning Iterative Models D3feat: Joint learning of dense detection and description of 3d local features,

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:10.705712Z

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