Paper Citation Record · LEDGER

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:1909.01622.

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

pith.paper-citation-record.v1
1909.01622 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:15:35.383002Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:15:35.194016Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T05:15:35.436178Z

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy35
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External citation measurements

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Outbound references

Observation 5fb65229-d075-442c-b0fc-52f98ae11c55 · outbound

This paper cites Towards Interpretable Polyphonic Transcription with Invertible Neural Networks.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Towards Interpretable Polyphonic Transcription with Invertible Neural Networks

Reference 1

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Observation 0f66c4c9-63ea-45fa-b632-8f94dd132aaa · outbound

This paper cites nuisance.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks nuisance

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:36.023107Z

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Observation 487c6069-7dfc-44f6-ab92-582a3ce1f5e8 · outbound

This paper cites Our notation also loosely follows the one used in [1].

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Our notation also loosely follows the one used in [1]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:36.008860Z

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Observation 5d4177d3-ad52-4dbd-825e-c1134e9b7de7 · outbound

This paper cites (L)”. The INN has 5 invertible layers, and 990.720 parameters in total. The parameter counts for the recurrent model variants “GRU (S).

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks (L)”. The INN has 5 invertible layers, and 990.720 parameters in total. The parameter counts for the recurrent model variants “GRU (S)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.993679Z

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Observation 52f1941b-15f3-4160-8127-c7b80a5ff05e · outbound

This paper cites A qualitative investigation of the direct interpretability of outputs back in input space was conducted.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks A qualitative investigation of the direct interpretability of outputs back in input space was conducted

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.977724Z

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Observation f9c2c959-5e39-46d3-88d4-ab84a34792f3 · outbound

This paper cites This research has received fund- ing from the European Research Council (ERC) under the European Union’s Horizon 2020 research and inno- vation programme (Grant agreement No.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks This research has received fund- ing from the European Research Council (ERC) under the European Union’s Horizon 2020 research and inno- vation programme (Grant agreement No

Reference 6

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Observation f0536031-32f0-4498-a7d4-4dd1416a98e8 · outbound

This paper cites Analyzing Inverse Problems with In- vertible Neural Networks.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Analyzing Inverse Problems with In- vertible Neural Networks

Reference 7

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Observation d4fa6695-d87e-402a-b9af-28986a5ebe10 · outbound

This paper cites Baird, D.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Baird, D

Reference 8

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Observation e6f552e7-b8cc-4eab-94a4-2ea2a2f408b1 · outbound

This paper cites madmom: a new Python Audio and Music Signal Processing Library.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks madmom: a new Python Audio and Music Signal Processing Library

Reference 9

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Observation 1ab106d3-2cab-4c9c-8089-7f15e2d0cbb5 · outbound

This paper cites Sliced and Radon Wasserstein Barycenters of Measures.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Sliced and Radon Wasserstein Barycenters of Measures

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.906427Z

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Observation af592de5-a83d-42ba-b764-48f37896c8e6 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Sta- tistical Machine Translation.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Learning Phrase Representations using RNN Encoder-Decoder for Sta- tistical Machine Translation

Reference 11

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Observation b5d29906-98bb-4f85-8978-56f45efad21f · outbound

This paper cites Nonlinear higher- order statistical decorrelation by volume-conserving neural architectures.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Nonlinear higher- order statistical decorrelation by volume-conserving neural architectures

Reference 12

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Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Unresolved cited work

Reference 13

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Observation c9a6a2e2-f281-433d-ac65-7d8aee09c859 · outbound

This paper cites NICE: non-linear independent components estimation.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks NICE: non-linear independent components estimation

Reference 14

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Observation de837ebf-bc9b-44ac-b15d-29feb3afa22a · outbound

This paper cites Density estimation using Real NVP.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Density estimation using Real NVP

Reference 15

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Observation ad3e11c8-2f94-4187-ad1e-15006351c6fa · outbound

This paper cites Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle

Reference 16

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Observation 3911b68d-07b6-4de7-89a0-97ce8eede690 · outbound

This paper cites Tabak and Eric Vanden-Eijnden.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Tabak and Eric Vanden-Eijnden

Reference 17

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Observation 3bf94e2f-79c0-48e6-a78d-60b1af6eb21d · outbound

This paper cites Onsets and Frames: Dual- Objective Piano Transcription.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Onsets and Frames: Dual- Objective Piano Transcription

Reference 18

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Observation ab5f5abd-5e90-496c-a542-448429007073 · outbound

This paper cites Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.770890Z

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Observation 1d109140-12c8-4579-b2db-7c47569b53d9 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Deep Residual Learning for Image Recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.756044Z

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Observation 92ab3a87-3895-49b5-96a9-2852950fd943 · outbound

This paper cites Long Short- Term Memory.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Long Short- Term Memory

Reference 21

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Observation 680ea904-032f-4b26-953d-64d16b3563e7 · outbound

This paper cites Excessive Invariance Causes Adversarial Vulnerability.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Excessive Invariance Causes Adversarial Vulnerability

Reference 22

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Observation 89293383-025b-4fcf-b7c0-1365da223654 · outbound

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Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Unresolved cited work

Reference 23

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Observation eb6b216b-a8b0-4116-bce6-ebb804d2200e · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 24

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Observation 65adaf9c-120a-4367-b1cf-82fad1e02ae7 · outbound

This paper cites On the Potential of Simple Framewise Approaches to Pi- ano Transcription.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks On the Potential of Simple Framewise Approaches to Pi- ano Transcription

Reference 25

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Observation 111ecfe7-c25d-4a41-82c8-45082810772a · outbound

This paper cites An Experimen- tal Analysis of the Entanglement Problem in Neural- Network-based Music Transcription Systems.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks An Experimen- tal Analysis of the Entanglement Problem in Neural- Network-based Music Transcription Systems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.664618Z

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Observation 97cc6e37-5a0b-40da-b4a3-63cd4570d9af · outbound

This paper cites Kingma and Prafulla Dhariwal.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Kingma and Prafulla Dhariwal

Reference 27

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Observation f4954b85-70b7-468d-a18f-fd04435af554 · outbound

This paper cites Pope, Charles E.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Pope, Charles E

Reference 28

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Observation 38dab15e-1a5c-4e54-a0e2-f1f460d20cbd · outbound

This paper cites Sturm, and Simon Dixon.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Sturm, and Simon Dixon

Reference 29

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Observation 923060c9-4913-455a-b620-67b626396238 · outbound

This paper cites Tal- walkar.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Tal- walkar

Reference 30

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Observation 43c05be0-a254-42b4-a199-663fc70c9f29 · outbound

This paper cites Wasserstein Barycenter and Its Application to Texture Mixing.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Wasserstein Barycenter and Its Application to Texture Mixing

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.591523Z

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Observation c1ef4d75-d48d-4311-8ac3-4aa4b5d93c7e · outbound

This paper cites Varia- tional Inference with Normalizing Flows.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Varia- tional Inference with Normalizing Flows

Reference 32

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Observation 63fc0e03-8ca1-4795-bc85-fab2e49f69bb · outbound

This paper cites Why Should I Trust You?.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Why Should I Trust You?

Reference 33

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Observation 44398a64-39f9-4f48-90df-c8be450717f8 · outbound

This paper cites Selvaraju, Michael Cogswell, Ab- hishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Selvaraju, Michael Cogswell, Ab- hishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.550421Z

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Observation e8beb820-9cec-48ac-99c5-aed12daa9ede · outbound

This paper cites An End-to-End Neural Network for Polyphonic Piano Music Transcription.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks An End-to-End Neural Network for Polyphonic Piano Music Transcription

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.535352Z

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Observation 0255d993-a940-4e1b-8eed-c5c5f0dd7d3a · outbound

This paper cites Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhut- dinov.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhut- dinov

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.518685Z

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Observation 93213997-1b10-4ede-be8b-7a5e739b02c7 · outbound

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Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Unresolved cited work

Reference 37

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Observation bf5d5b33-77b6-47db-866c-bd605a8aabb0 · outbound

This paper cites Ax- iomatic Attribution for Deep Networks.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Ax- iomatic Attribution for Deep Networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.487533Z

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Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Unresolved cited work

Reference 39

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Observation a1d998fd-3713-4caa-aad7-031783f4e2c5 · outbound

This paper cites Improving Variational Auto-Encoders using Householder Flow.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Improving Variational Auto-Encoders using Householder Flow

Reference 40

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This paper cites Tomczak, and Max Welling.

Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Tomczak, and Max Welling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:15:35.458256Z

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Pith citing papers

Observation 5fb65229-d075-442c-b0fc-52f98ae11c55 · inbound