HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories | Paper Citation Record

Observation 80ff1b95-789a-40fc-b718-1d1de1b8537f · outbound

This paper cites Learning representations and generative models for 3d point clouds.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning representations and generative models for 3d point clouds

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.091312Z

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 12fbfca2-bdd1-42b0-a555-9f85500d9372 · outbound

This paper cites Hyperfields: To- wards zero-shot generation of nerfs from text.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperfields: To- wards zero-shot generation of nerfs from text

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.080424Z

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 966d194a-c90f-42a9-bcf9-90eca69867a7 · outbound

This paper cites Dickson, Ryan M.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Dickson, Ryan M

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.069965Z

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 916be7a8-3483-48ca-b8d9-c13663a9e6ea · outbound

This paper cites an unresolved cited work.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:57:14.059195Z

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 4a813a68-2e2e-4e31-bb8a-57cd2c8cf19c · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Emerg- ing properties in self-supervised vision transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.255389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c3ce117-1785-44f8-84f6-036d5486d5e0 · outbound

This paper cites Stargan v2: Diverse image synthesis for multiple domains.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Stargan v2: Diverse image synthesis for multiple domains

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.041699Z

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 d6706af2-da35-447f-9735-f5b21cd1e580 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Objaverse: A universe of annotated 3d objects

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.030447Z

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 d7c6cc5d-183a-4f78-8343-6ee91fcb840e · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.019839Z

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 9196c520-1d7e-4c58-9040-3a3213dd6206 · outbound

This paper cites Interpreting the Weight Space of Customized Diffusion Models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Interpreting the Weight Space of Customized Diffusion Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.270323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d636eb9-e69b-42ca-8006-373347a1347b · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Implicit generation and mod- eling with energy based models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:14.008274Z

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 e40b9c4b-7b13-42e5-9491-46c8b0018005 · outbound

This paper cites Hyperdiffusion: Generating implicit neural fields with weight-space diffusion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperdiffusion: Generating implicit neural fields with weight-space diffusion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.996096Z

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 d028cc85-93ca-4dcc-8be9-d21fc3e3cdc5 · outbound

This paper cites One Step Diffusion via Shortcut Models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories One Step Diffusion via Shortcut Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.281856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf262ccf-300b-4bbd-bacb-3b097b9cbb7a · outbound

This paper cites An image is worth one word: Personalizing text-to-image gen- eration using textual inversion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories An image is worth one word: Personalizing text-to-image gen- eration using textual inversion

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.985718Z

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 99c2c018-8491-49fa-a418-6f748c6db5f1 · outbound

This paper cites Learning energy-based models by dif- fusion recovery likelihood.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning energy-based models by dif- fusion recovery likelihood

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.975977Z

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 fc235ab9-2c23-496b-82fe-b83b7f5b8795 · outbound

This paper cites Atlasnet: A papier-m ˆach´e ap- proach to learning 3d surface generation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Atlasnet: A papier-m ˆach´e ap- proach to learning 3d surface generation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.965536Z

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 cc6f6cb5-20ac-48ec-9070-812132081d79 · outbound

This paper cites Hypernetworks.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hypernetworks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.954612Z

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 7eaf8984-e3a6-4824-8799-423113361f43 · outbound

This paper cites Denoising dif- fusion probabilistic models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Denoising dif- fusion probabilistic models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.299327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80079071-87be-4c30-8c74-01ac6e89f0ef · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories LoRA: Low-rank adaptation of large language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.302749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fabf6ce8-0f29-45cd-85f4-ac44e028cf1b · outbound

This paper cites Mani- foldplus: A robust and scalable watertight manifold surface generation method for triangle soups.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Mani- foldplus: A robust and scalable watertight manifold surface generation method for triangle soups

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.931915Z

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 22820417-5370-4179-b82e-7e8078a825d7 · outbound

This paper cites Shap-e: Generating condi- tional 3d implicit functions.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Shap-e: Generating condi- tional 3d implicit functions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.921587Z

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 fc456880-76a8-48ce-99d7-d949adfb515e · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Progressive growing of gans for improved quality, stability, and variation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.911400Z

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 500acbc8-996f-421c-ad5f-07dfd2a23ce3 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Elucidating the design space of diffusion-based generative models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.900962Z

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 033f55d8-9f4f-417d-a5b8-5508ead885ee · outbound

This paper cites Consistency trajectory mod- els: Learning probability flow ode trajectory of diffusion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Consistency trajectory mod- els: Learning probability flow ode trajectory of diffusion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.890095Z

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 7fff3b7b-a405-43e3-b1d3-ef774db687d2 · outbound

This paper cites A tutorial on energy-based learn- ing.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories A tutorial on energy-based learn- ing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.879325Z

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 6bee638d-11c1-4344-81da-4b533097a0fc · outbound

This paper cites an unresolved cited work.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:57:13.867748Z

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 9d91600a-9e83-42d1-a131-7949451a8bf2 · outbound

This paper cites Zero-1-to- 3: Zero-shot one image to 3d object.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Zero-1-to- 3: Zero-shot one image to 3d object

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.857022Z

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 8ee77d07-533d-47c0-b68e-46875d2ed0e1 · outbound

This paper cites Deep learning face attributes in the wild.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deep learning face attributes in the wild

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.847235Z

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 681b5cf8-9d0a-4b55-a4b1-a6641608c79f · outbound

This paper cites Marching cubes: A high resolution 3d surface construction algorithm.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Marching cubes: A high resolution 3d surface construction algorithm

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.836285Z

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 76e66998-d4e0-4a75-8581-cefe7c46a015 · outbound

This paper cites Occupancy networks: Learning 3d reconstruction in function space.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Occupancy networks: Learning 3d reconstruction in function space

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.826393Z

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 f7600340-c068-4fee-9e02-5f191d90260b · outbound

This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deepsdf: Learning con- tinuous signed distance functions for shape representation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.815361Z

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 a2b8c74a-a593-49e1-8721-ead44675fe81 · outbound

This paper cites Scalable diffusion models with transformers.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Scalable diffusion models with transformers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.342127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee12a8f1-0f5f-47d0-b3ee-d9c264cbb00b · outbound

This paper cites Hypermaml: Few-shot adaptation of deep models with hypernetworks.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hypermaml: Few-shot adaptation of deep models with hypernetworks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.798945Z

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 4a59d79b-2cc6-4184-92f8-4e03240bd3b5 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.787760Z

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 736586cc-bc09-4375-94c6-c12344bffe8e · outbound

This paper cites Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to- 3d.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to- 3d

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.776780Z

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 3cc1d364-5572-4e47-a82f-01762e734e20 · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning transferable visual models from natural language supervision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.355744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04c6c33d-1127-4a7e-95fd-f4bbe01ba180 · outbound

This paper cites Physics informed deep learning (part i): Data-driven solu- tions of nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics informed deep learning (part i): Data-driven solu- tions of nonlinear partial differential equations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.759661Z

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 cd4a8362-8b25-411f-8131-5bc3008deb2d · outbound

This paper cites Physics informed deep learning (part ii): Data-driven discov- ery of nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics informed deep learning (part ii): Data-driven discov- ery of nonlinear partial differential equations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.748531Z

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 75000173-9e59-422a-a53c-63c39c317027 · outbound

This paper cites Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.736721Z

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 ee72d0b4-45ad-4996-9df7-fab2f5177b67 · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories High-resolution image syn- thesis with latent diffusion models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.723960Z

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 69338018-6d26-47eb-9d90-fcb1fa1930df · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.608261Z

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 f70fda7c-aff8-4a2b-941f-2fea29607a9f · outbound

This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.596402Z

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 d7916f89-4adc-46e6-a85a-09e2bc8f1753 · outbound

This paper cites Learning representations by back-propagating er- rors.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning representations by back-propagating er- rors

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.584581Z

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 d63cb8ac-8cdd-4090-84cc-bd0335f29851 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Photorealistic text-to-image diffusion models with deep language understanding

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.572753Z

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 bf7db5da-d4c5-49f9-aaba-611aa4dfa267 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deep unsupervised learning using nonequilibrium thermodynamics

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.388817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0c5a7f5-d3ea-4f14-8d2c-5c68dc9cb715 · outbound

This paper cites Generative modeling by esti- mating gradients of the data distribution.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Generative modeling by esti- mating gradients of the data distribution

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.555329Z

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 dd1612ab-a317-4fc0-b046-7ca99773efe2 · outbound

This paper cites Maximum likelihood training of score-based diffusion mod- els.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Maximum likelihood training of score-based diffusion mod- els

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.544073Z

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 2f32bc55-1bba-412a-8c8c-038ef3eb48d3 · outbound

This paper cites Consistency models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Consistency models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.533246Z

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 73cc9da7-032e-4071-8682-be8dac2f821e · outbound

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories A connection between score matching and denoising autoencoders

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:13.402512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 003a7a36-6063-496d-8cd4-34692e7c781d · outbound

This paper cites Grewe, and Joao Sacramento.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Grewe, and Joao Sacramento

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.515414Z

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 e3a1c885-1076-452e-a5a8-2c2bae2c8e6c · outbound

This paper cites Pointflow: 3d point cloud generation with continuous normalizing flows.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Pointflow: 3d point cloud generation with continuous normalizing flows

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.504169Z

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 45f9a553-e0d1-4a62-b71d-ed47105df98e · outbound

This paper cites Graph hy- pernetworks for neural architecture search.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Graph hy- pernetworks for neural architecture search

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.492795Z

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 3bd260b5-ba29-4811-9dbc-6c5cbb39aeef · outbound

This paper cites Reconstruction loss.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Reconstruction loss

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.482333Z

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 50a28051-1b22-40ab-8a83-2dbbb776a73e · outbound

This paper cites Specifically, we include images from the AFHQ dataset [6] sampled directly from the hypernetwork (Fig- ure 10) and after fast fine-tuning (Figure 11).

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Specifically, we include images from the AFHQ dataset [6] sampled directly from the hypernetwork (Fig- ure 10) and after fast fine-tuning (Figure 11)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.472667Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers