Paper Citation Record · LEDGER

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations

As of 17 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2505.23942.

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

pith.paper-citation-record.v1
2505.23942 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:04.013182Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

24 of 24 outbound references displayed

verified exact1
verified fuzzy7
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df87988c-144e-4890-a05c-9c82425d618b · outbound

This paper cites The generalized sigmoid activation function: Competitive supervised learning.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations The generalized sigmoid activation function: Competitive supervised learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:02.121727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91ac5d94-3e34-463e-8c85-9f9b88fd88a6 · outbound

This paper cites LeCun, L \'e on Bottou, Genevieve B.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations LeCun, L \'e on Bottou, Genevieve B

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:02.182353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ba49c52-daeb-4b62-8940-320a9349bc40 · outbound

This paper cites an unresolved cited work.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:42:05.822536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 49609d34-7153-4723-b0ea-b60a76983cc1 · outbound

This paper cites an unresolved cited work.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:42:05.679909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5bbd54c8-e567-4f12-9aea-7bd02f5a2e15 · outbound

This paper cites Gaussian error linear units (gelus).

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Gaussian error linear units (gelus)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:05.501003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c035c418-9142-4685-a091-68da37bcb363 · outbound

This paper cites E fficient N et: Rethinking model scaling for convolutional neural networks.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations E fficient N et: Rethinking model scaling for convolutional neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:05.449194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3412cf3a-d44e-4135-a238-e1c2a0d7583f · outbound

This paper cites Attention Is All You Need.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Attention Is All You Need

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:02.655820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51f37ccd-b75b-4a4f-8d93-b134d51fe8c0 · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images , 1 2009.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Learning Multiple Layers of Features from Tiny Images , 1 2009

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:05.320678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cef43ca7-c1db-4f93-8b3c-35b4285707d2 · outbound

This paper cites Maas, Raymond E.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Maas, Raymond E

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:05.180122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05e0c8a7-75f1-4fa5-905d-e5eca1cfe0ae · outbound

This paper cites Findings of the 2014 workshop on statistical machine translation.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Findings of the 2014 workshop on statistical machine translation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:05.086755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d535201-72fa-42d1-a311-809f4b588208 · outbound

This paper cites Deep Residual Learning for Image Recognition.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Deep Residual Learning for Image Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.012124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2409fec4-ec93-4ba9-ba54-17e7e0c1e971 · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations BERT: pre-training of deep bidirectional transformers for language understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.110775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec0377f5-9eca-4f40-a175-605b920e2ae4 · outbound

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

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.179099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cb5d3d9-d5b2-4e20-9b9c-70f4dfdf90e5 · outbound

This paper cites A Robustly Optimized BERT Pre-training Approach with Post-training.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations A Robustly Optimized BERT Pre-training Approach with Post-training

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T12:42:04.143622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc7ad223-005a-4f3a-a7db-8718d0e91eed · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.381498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 022fa4f3-d6a5-45fe-8771-cec103757ca0 · outbound

This paper cites Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T12:42:04.392172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e33387b-a5cd-4fe0-8ccc-6ff22f0ce5a7 · outbound

This paper cites Mish: A Self Regularized Non-Monotonic Activation Function.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Mish: A Self Regularized Non-Monotonic Activation Function

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.531466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16e93cfb-354f-41dd-baf1-82cbb7035824 · outbound

This paper cites An overview of gradient descent optimization algorithms.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations An overview of gradient descent optimization algorithms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.624328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39de25c3-c34e-47f7-8ce1-91d5217d2e51 · outbound

This paper cites Maas, Raymond E.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Maas, Raymond E

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:04.873770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d575e59-e978-4c88-8cd0-e6585c999ee6 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Adam: A Method for Stochastic Optimization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.788527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3426d83b-7592-48ea-813d-05f87c55fb01 · outbound

This paper cites an unresolved cited work.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:42:04.721925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf7e89c4-6cb3-437a-8731-d3f1a3712259 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.899104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed692a4c-9576-469b-80fd-a87fa58a2d89 · outbound

This paper cites Activation functions in deep learning: A comprehensive survey and benchmark.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Activation functions in deep learning: A comprehensive survey and benchmark

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.956037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af4e0f2b-7617-44ca-8cf0-973e8dab0a81 · outbound

This paper cites Adaptive parametric activation.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Adaptive parametric activation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:04.587498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.