KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks | Paper Citation Record

Observation 6bcd5434-203d-47f5-9db7-9eb23b205b25 · outbound

This paper cites Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud

Reference 1

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Observation 751a5659-3f38-4e81-9f06-985effa6dca3 · outbound

This paper cites Deep learning.Nature, 521(7553):436–444, 2015.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Deep learning.Nature, 521(7553):436–444, 2015

Reference 2

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Observation 15951f7c-9b8d-4394-8751-37fdb6097563 · outbound

This paper cites Opening the black box: low-dimensional dynamics in high- dimensional recurrent neural networks.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Opening the black box: low-dimensional dynamics in high- dimensional recurrent neural networks

Reference 3

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Observation d927423c-d749-4b38-b829-78bbadb23d9b · outbound

This paper cites Farrell, S.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Farrell, S

Reference 4

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Observation b4491594-e1b0-4b8f-859a-c8e3b9f29fcc · outbound

This paper cites Driscoll, Krishna V.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Driscoll, Krishna V

Reference 5

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Observation e3155769-3c82-4f9f-8b14-73189e193fad · outbound

This paper cites The simplicity bias in multi-task rnns: Shared attractors, reuse of dynamics, and geometric representation.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks The simplicity bias in multi-task rnns: Shared attractors, reuse of dynamics, and geometric representation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:56.916455Z

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Observation 9503b921-12e3-4923-b62a-7533116c8135 · outbound

This paper cites The interplay between randomness and structure during learning in rnns.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks The interplay between randomness and structure during learning in rnns

Reference 7

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Observation 7a62f08e-66c4-4eaa-9225-d562b818cb3d · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 8

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Observation 5a3f862a-bab8-46b5-946c-f0713fa1bdb8 · outbound

This paper cites Hodgkin and A.F.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Hodgkin and A.F

Reference 9

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 10

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Observation b76bca2a-fe1f-4fb4-95e5-66f358994d31 · outbound

This paper cites Neural machine translation by jointly learning to align and translate, 2016.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Neural machine translation by jointly learning to align and translate, 2016

Reference 11

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Observation 0dc743c5-477b-4a95-8999-72e80ddad49f · outbound

This paper cites van der Schaft.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks van der Schaft

Reference 12

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Observation edf50c3d-68f4-4e11-9552-bbdc10940332 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Neural tangent kernel: Convergence and generalization in neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:55.584613Z

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Observation b8b711e3-95ec-40d4-9e0f-f31e0a28d2b3 · outbound

This paper cites The recurrent neural tangent kernel.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks The recurrent neural tangent kernel

Reference 14

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This paper cites Exploring the impact of activation functions in training neural ODEs.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Exploring the impact of activation functions in training neural ODEs

Reference 15

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Observation b089b806-a031-4afa-8f85-824f4319dfc2 · outbound

This paper cites Deep residual learning for image recognition, 2015.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Deep residual learning for image recognition, 2015

Reference 16

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Observation 5cd33bfc-d565-4711-be70-6d82366257c2 · outbound

This paper cites On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools

Reference 17

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 18

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 19

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 20

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This paper cites Zavatone-Veth.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Zavatone-Veth

Reference 21

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 22

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Observation 08929efc-2a2f-4be5-bc2e-cf5b21138b13 · outbound

This paper cites Transition to chaos in random neuronal networks.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Transition to chaos in random neuronal networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:54.026473Z

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Observation 866c8fed-1612-4292-b8f4-f27a86f65541 · outbound

This paper cites How connectivity structure shapes rich and lazy learning in neural circuits.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks How connectivity structure shapes rich and lazy learning in neural circuits

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:53.858218Z

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Observation f8f35350-1759-4c2d-b31e-8619b307bda1 · outbound

This paper cites Organiz- ing recurrent network dynamics by task-computation to enable continual learning.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Organiz- ing recurrent network dynamics by task-computation to enable continual learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:53.677361Z

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Observation 28e9b5fe-29c3-4422-8de5-22dfc29fb4ed · outbound

This paper cites Learning representations by back-propagating errors.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Learning representations by back-propagating errors

Reference 26

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This paper cites Kistler, Richard Naud, and Liam Paninski.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Kistler, Richard Naud, and Liam Paninski

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:53.349987Z

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This paper cites Mean-field theory of two-layer neural networks: dimension-free bounds and kernel limit.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Mean-field theory of two-layer neural networks: dimension-free bounds and kernel limit

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:53.165255Z

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Observation ca283e1c-4659-4146-bbf9-c99dac9b35a2 · outbound

This paper cites Context-dependent computation by recurrent dynamics in prefrontal cortex.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Context-dependent computation by recurrent dynamics in prefrontal cortex

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:53.023125Z

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This paper cites Open the black box of recurrent neural network by decoding the internal dynamics.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Open the black box of recurrent neural network by decoding the internal dynamics

Reference 30

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This paper cites Izhikevich.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Izhikevich

Reference 31

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 32

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This paper cites Exploring flip flop memories and beyond: Training recurrent neural networks with key insights.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Exploring flip flop memories and beyond: Training recurrent neural networks with key insights

Reference 33

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This paper cites Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion

Reference 34

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 35

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This paper cites Predictive learning as a network mechanism for extracting low-dimensional latent space representations.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Reference 36

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This paper cites Pereira, and William Bialek.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Pereira, and William Bialek

Reference 37

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Observation af704ddb-f153-4999-b777-e0eb75762b5b · outbound

This paper cites Joglekar, Henry F.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Joglekar, Henry F

Reference 38

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Observation b6916a2d-9ae4-4372-80b6-9f889c441dd5 · outbound

This paper cites Out-of-Domain Generalization in Dynamical Systems Reconstruction.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Out-of-Domain Generalization in Dynamical Systems Reconstruction

Reference 39

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Observation 663c856a-2f82-4c5d-90b7-5d0a8de208fc · outbound

This paper cites Ergodic theory of differentiable dynamical systems.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Ergodic theory of differentiable dynamical systems

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:51.483380Z

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Observation e22ae7ba-5102-422f-84fc-3bc742a276b9 · outbound

This paper cites Finite-time lyapunov exponents of deep neural networks.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Finite-time lyapunov exponents of deep neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:51.336343Z

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Observation f72e09cb-db5d-4dd3-8df5-3317c0eb127c · outbound

This paper cites On the difficulty of learning chaotic dynamics with rnns.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks On the difficulty of learning chaotic dynamics with rnns

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:51.158337Z

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Observation 2fd4d2ec-bc34-4faa-940b-5048d9c9848d · outbound

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 43

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Observation 1a5affb0-b3ac-47b2-9c21-4ff3844ba3f0 · outbound

This paper cites On the difference between variational and unitary coupled cluster theories.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks On the difference between variational and unitary coupled cluster theories

Reference 44

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This paper cites Optimal Stopping and the Sufficiency of Randomized Threshold Strategies.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Optimal Stopping and the Sufficiency of Randomized Threshold Strategies

Reference 45

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Observation a5c4c323-278e-4063-aecb-ec820ed6fa92 · outbound

This paper cites Discretize-optimize vs.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Discretize-optimize vs

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:50.708663Z

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Observation 35d5db53-b68d-40f3-921c-f75a149a6bef · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 47

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Observation 2ec0a005-f3d0-478f-9485-b5035a8c9005 · outbound

This paper cites Golub and Charles F.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Golub and Charles F

Reference 48

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This paper cites Introductory Functional Analysis with Applications.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Introductory Functional Analysis with Applications

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:50.261126Z

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Observation 1784fa58-995f-4304-bd87-1119166edc4e · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

Reference 50

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This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks JAX: composable transformations of Python+NumPy programs, 2018

Reference 51

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KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:50.098234Z

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Observation c77261cf-996b-4d13-ac77-469fed33f12e · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:49.842177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d165a6fd-a44c-4b04-9457-6f7a549c9b66 · outbound

This paper cites Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:43.804067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c5a6cfe-3e1e-48c3-9f6b-08afc86ca7b4 · outbound

This paper cites per-trial trajectories.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks per-trial trajectories

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:49.617299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6de78107-afbc-4548-af0a-bd560277e5ec · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:49.410199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation acdb671c-4e22-442a-a492-6af5fd9d72ff · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:49.166800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 66353913-9291-4d7f-a4a4-63ec20d59341 · outbound

This paper cites extrapolating.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks extrapolating

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:48.918598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1bac7ea-b1ad-401c-b7af-262289526aa6 · outbound

This paper cites direct integral.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks direct integral

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:48.789270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec660845-8aff-421f-8d38-59cecf229a08 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:48.559397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d8c4a162-32dc-4fcc-89f9-e29b731ce77d · outbound

This paper cites The linear approach relies on the variation of parameters for small inputs q.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks The linear approach relies on the variation of parameters for small inputs q

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:48.366664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26e8cabc-8e58-4c48-bb7f-7340c1e33ca0 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:48.103873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b8b4be57-70f9-4ae7-b243-a242a303c056 · outbound

This paper cites View" of Computation Graph Wj Supplementary Figure 9: Visual depiction of computation graph “view.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks View" of Computation Graph Wj Supplementary Figure 9: Visual depiction of computation graph “view

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:14:47.852542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46862ad1-e2ed-4e6c-9c45-c0f9abea96eb · outbound

This paper cites block arrowhead matrix.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks block arrowhead matrix

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:47.609967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 732e2c43-c370-47ef-b7cc-518fb5bb39f3 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:47.410535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af347287-25b0-4acd-ab96-dad6c0de486b · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:47.148945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0198fb55-9298-4ddd-8daf-778baef5addb · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:47.083469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4aec197-4259-45ce-bbc9-7726f03cebfd · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:47.005723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cdd03140-a0a1-4d47-babe-a0cf3cce8b14 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.910077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e19e4cd-1fcf-413e-8556-0dee65e90f05 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.813146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96dedfa9-f3fb-4df9-ae13-4730166706b8 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.695009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 485000c7-b466-41bd-a074-dba0ab1cff89 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.555791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b42a4bd-03af-4872-92ab-9ab3588b406d · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.452308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab01e0fb-89e0-45c4-bef9-99b81cef4216 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.345240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dcb70147-91f1-4f12-8dbb-a1cf5251f03d · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.250178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation abdcdedf-4fc1-4fc5-a9cf-b5253bf04e00 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.122831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ecfb125-af0d-4a11-9a76-eb20603666e1 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:46.026698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ecb72c83-a967-40ae-9fda-00c158797b7c · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:45.931489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1573e2f9-a50f-43a2-a648-3d4b08193d29 · outbound

This paper cites an unresolved cited work.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:14:45.838571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d434a528-97c3-43fe-9fba-63080984e24a · outbound

This paper cites Answer: [NA] Justification: LLMs are not a core component of our research and they were not used in generation of the paper.

KPFlow: An Operator Perspective on Dynamic Collapse Under Gradient Descent Training of Recurrent Networks Answer: [NA] Justification: LLMs are not a core component of our research and they were not used in generation of the paper

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:14:45.725074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers