Paper Citation Record · LEDGER

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2605.27097.

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

pith.paper-citation-record.v1
2605.27097 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:51:45.506520Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

50 of 50 outbound references displayed

verified exact1
verified fuzzy0
unresolved45
parse uncertain3
malformed identifier0
metadata mismatch1

External citation measurements

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Outbound references

Observation 2c83baa8-6d41-44bd-b159-539bad06401f · outbound

This paper cites Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:53:51.159879Z

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 b523a32e-8bda-4e75-8aa1-6142930be8b0 · outbound

This paper cites On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:53:51.164849Z

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Observation 82ed6303-6df6-4667-870b-f0cfe20e23cb · outbound

This paper cites Wide Residual Networks.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Wide Residual Networks

Reference 3

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Observation 99cae1f2-e14e-4c92-8c69-bee799863e59 · outbound

This paper cites an unresolved cited work.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 4

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Observation 941d7f3a-21fe-4bcc-b571-47045cceeadb · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 1a4b21a3-75c1-4412-99f4-391caee98f02 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 6

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Observation 1f178cd5-5a5e-480f-8243-c50e479b49a8 · outbound

This paper cites In the above, and throughout the proof, we use ∥·∥ to denote the ℓ2-norm.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias In the above, and throughout the proof, we use ∥·∥ to denote the ℓ2-norm

Reference 7

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Observation 1db02034-0c02-4018-a540-1215b08dd9bf · outbound

This paper cites an unresolved cited work.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 8

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Observation e8cf699d-ea8d-4150-b9c2-0da95e06de5c · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 9

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Observation a00995a5-30fd-44aa-9d22-bba3aa229e23 · outbound

This paper cites The monotonicity of the gradient flow means there exists a maximum rate of growth it is possible for any neuron to achieve.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias The monotonicity of the gradient flow means there exists a maximum rate of growth it is possible for any neuron to achieve

Reference 10

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Observation 50aa39f5-dfca-4b1c-950c-6c7cd26e0a5c · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation a2f718d5-d12f-4918-a801-b25b4e40bb5f · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 806a42ce-3dc4-450f-9638-35efd76ba544 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 72347cb2-40d3-4022-9445-76466ce62938 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation a2251994-b7b8-4249-861f-515ea8e92ffe · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 15fed945-9969-4620-b1a7-8241ff09cabd · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 16

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Observation f61147eb-d1f4-4e43-a935-017814935413 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 17

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Observation a23550ed-5be3-42bb-b43f-3594f86f3106 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 18

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Observation b903ff6d-2735-4dd9-ab02-fb9251ef6484 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation f1855811-0e0b-4b79-9a5a-6eebaaac46d2 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 20

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Observation b68bff6a-bf42-48dd-8c71-c566a8ce8271 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 21

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Observation 5baa6ac0-4c63-42c3-91ca-f3c175e3634b · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 22

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Observation e96a58c6-d608-4170-9224-9a23091633cd · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 610021f2-d132-4d86-a864-10b864d74cdd · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation edf502e5-35dc-4fbe-8dac-f1aeaca47564 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 273b3766-9d14-47aa-b194-b05e572964ec · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 98a57c38-f59d-4a45-9cc1-671f002be2ad · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation e6d32ed9-21ae-4484-b73c-6a71a2f83c5d · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation be65d8a1-ede2-4a1e-8c5a-08c787e3805f · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation f7fb0a18-58c3-45f0-a1f7-7de41b7c0573 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation b0ce6570-2aff-4a87-ba31-514da4eb93fe · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 6669d42d-52b2-43c6-91a0-591301948ca8 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation aa43c9f9-d701-4221-9690-8216754833c0 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 67eebdd4-2733-4940-afc2-b2a8e5f12c8a · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 3edc376c-6f8e-41a3-92a9-5cb0c94df1dd · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 35

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Observation bd22e664-41a8-4f8b-a831-af35b1fb0686 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 51b96feb-d73a-4eee-890b-b50e802871ba · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 7533d038-ca31-4841-9907-08754240de77 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 7cf9f507-cbea-4838-b310-ded584a6d863 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation ed7fa00d-afc1-4928-b75b-0b9d056f80eb · outbound

This paper cites Then given any κ′ ∈ [κ, ∞),eθα converges uniformly to θ◦ on [κ, κ′].

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Then given any κ′ ∈ [κ, ∞),eθα converges uniformly to θ◦ on [κ, κ′]

Reference 40

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Observation ae3d163f-6eb3-4f78-9823-f236339563b1 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation edf62118-d481-443a-bcbc-794ac3466f1c · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 4e7f4fa0-59ec-4803-ad3b-b981c925d2c3 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation a0854c98-88d3-49a6-9425-8c6cc1020e50 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 44

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Observation f08e37f3-aef2-4dfb-93e1-6df80f1331c8 · outbound

This paper cites ■ Finally, we are able to provide a proof of Theorem 2.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias ■ Finally, we are able to provide a proof of Theorem 2

Reference 45

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Observation 9f41c45b-5b53-41be-90f0-01d20da6b02f · outbound

This paper cites Since |Isj | ≥ min(n−, n+) almost surely by definition, we then have that a column j is zero with probability at most 1 2 min(n−,n+).

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Since |Isj | ≥ min(n−, n+) almost surely by definition, we then have that a column j is zero with probability at most 1 2 min(n−,n+)

Reference 46

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Observation 6029aa24-cced-412a-b55a-5653d0424fb6 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation a0d697a6-98ad-42a3-8d18-d493f1bfa3d1 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

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Observation 8b61cfe0-95e3-48d7-944e-bb41496f6af9 · outbound

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Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Unresolved cited work

Reference 49

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Observation 31bb6a1e-d738-4714-aa71-499f7a96dce0 · outbound

This paper cites Then for every t ∈ [T1, T2], F ′(t) ≤ q√p + F ′(T1) exp(√p(t − T1)).

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias Then for every t ∈ [T1, T2], F ′(t) ≤ q√p + F ′(T1) exp(√p(t − T1))

Reference 50

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