Paper Citation Record · LEDGER

Understanding Nonlinear Implicit Bias via Region Counts in Input Space

As of 16 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2505.11370.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.11370 v3

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:02.791322Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy1
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8714496d-3788-4f9f-a3e0-13279e99558d · outbound

This paper cites Three Factors Influencing Minima in SGD.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Three Factors Influencing Minima in SGD

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.740773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d0bf97-238d-49d4-8e7e-7d2c8e9512c7 · outbound

This paper cites Fantastic Generalization Measures and Where to Find Them.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Fantastic Generalization Measures and Where to Find Them

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.749979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab2e66c2-eb76-42cf-b062-be5c12de22d6 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.758232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f067d619-b83b-4066-90ab-fe83b2a5087b · outbound

This paper cites The large learning rate phase of deep learning: the catapult mechanism.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space The large learning rate phase of deep learning: the catapult mechanism

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.762454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ee4068-e334-4085-ac08-c17aa93afba6 · outbound

This paper cites Gradient Descent Maximizes the Margin of Homogeneous Neural Networks.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.766758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 475a044e-d6d8-489f-826b-5f9658c239c9 · outbound

This paper cites Sensitivity and Generalization in Neural Networks: an Empirical Study.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Sensitivity and Generalization in Neural Networks: an Empirical Study

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.771159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64d02fd-e4e1-4f7e-b52d-3c82fb315389 · outbound

This paper cites A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.775111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e545c357-2187-4b1d-a919-917471337d54 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.778864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91d0d0fb-0eab-48bc-abbe-40de8eac3961 · outbound

This paper cites A Unifying View on Implicit Bias in Training Linear Neural Networks.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space A Unifying View on Implicit Bias in Training Linear Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.786477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81e4b656-bc4c-4382-8f82-0b1fd30ed138 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space mixup: Beyond Empirical Risk Minimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.791322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a123239b-eb85-4578-9658-a618050d267c · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.730896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba5a5c5e-d764-4c25-ae8c-fe7fe55f69d2 · outbound

This paper cites Don't Decay the Learning Rate, Increase the Batch Size.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Don't Decay the Learning Rate, Increase the Batch Size

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.782451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cddf30c8-4d23-4d25-ba13-b01ff571a493 · outbound

This paper cites Gradient descent aligns the layers of deep linear networks.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Gradient descent aligns the layers of deep linear networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.745231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 267893f8-f80a-4010-865a-444a383785a4 · outbound

This paper cites Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:02:02.975426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96648c8f-d905-4f8a-b830-5e503ab0e5f4 · outbound

This paper cites L., Julian, K., and Kochender- fer, M.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space L., Julian, K., and Kochender- fer, M

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:03.039751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 72ce4171-3c84-466e-aae6-18d88d9c1737 · outbound

This paper cites Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.722413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82f189e9-3258-425d-a049-34e9b632efe1 · outbound

This paper cites SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.718395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bcaca72-417c-4225-a637-44973cbd4692 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space Imagenet: A large-scale hierarchical image database

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.726763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16946eeb-05d1-4c40-a69f-8bb0af88332b · outbound

This paper cites A Modern Look at the Relationship between Sharpness and Generalization.

Understanding Nonlinear Implicit Bias via Region Counts in Input Space A Modern Look at the Relationship between Sharpness and Generalization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:02.713607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.