Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:55:07.449777Z
Paper Citation Record · LEDGER
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks
As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.01559.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact0
- verified fuzzy3
- unresolved12
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4cfff7a3-c752-40ba-a255-73c9b541887d · outbound
This paper cites The Early Phase of Neural Network Training.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks The Early Phase of Neural Network Training
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.338927Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e526253-e33e-4796-9f29-e21046fbbbbe · outbound
This paper cites Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.491787Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 38ae33e8-17d0-4c55-9e44-5ac48554e995 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Adam: A Method for Stochastic Optimization
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.798999Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d6148a2-7997-4081-8575-19338a6c64a4 · outbound
This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Quantizing deep convolutional networks for efficient inference: A whitepaper
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.896500Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af6c8a27-83d0-4216-95cd-47b8214c8b1c · outbound
This paper cites Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Reference 12
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T20:55:07.777497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d7979176-ab7e-4070-87c9-fe435b8584d6 · outbound
This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Instance Normalization: The Missing Ingredient for Fast Stylization
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:07.271643Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7a96b538-ddd4-4205-b63e-0b542597d65e · outbound
This paper cites Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:07.373741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 981798e2-a14b-4184-9bcc-9733e6ed7da2 · outbound
This paper cites A N ETWORK STRUCTURE Following Frati et al.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks A N ETWORK STRUCTURE Following Frati et al
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:55:08.197822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2b75cb47-9ca0-44d6-9713-e8e438c3f2c9 · outbound
This paper cites icarl: Incremental classifier and representation learning.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks icarl: Incremental classifier and representation learning
Reference 1964
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:55:08.513184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a0d18404-0de3-4e31-9944-71a96a853b96 · outbound
This paper cites Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.230453Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdcba208-0abf-479e-90c7-da7673d8cef1 · outbound
This paper cites Understanding intermediate layers using linear classifier probes.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Understanding intermediate layers using linear classifier probes
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.033898Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f6d35434-a71d-4493-86fe-c9ea17d0afef · outbound
This paper cites Meta-Learning Representations for Continual Learning.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Meta-Learning Representations for Continual Learning
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.666158Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 57704d56-bd8f-4a20-9f37-ca61fcb932c5 · outbound
This paper cites Learning to Continually Learn.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Learning to Continually Learn
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.104169Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3b14012-7c07-4888-8106-62ee5ff1cf64 · outbound
This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:07.002062Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f4d0e54b-5487-4104-b4ba-82c66c4fbb81 · outbound
This paper cites Reset it and forget it: Relearning last-layer weights improves continual and transfer learning.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks Reset it and forget it: Relearning last-layer weights improves continual and transfer learning
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:55:08.796711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f914b36a-fff1-41dd-bc88-3abc5e3a10d2 · outbound
This paper cites FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.577457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.