Paper Citation Record · LEDGER

Conditional Flow Variational Autoencoders for Structured Sequence Prediction

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 2 inbound Pith citation observations for arXiv:1908.09008.

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

pith.paper-citation-record.v1
1908.09008 v3

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:26:52.566534Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:22:07.956101Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:15:52.528615Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy7
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3fea0cdc-5336-4599-9a82-62d667c24c55 · outbound

This paper cites Variational Approaches for Auto-Encoding Generative Adversarial Networks.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.511492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3358e320-ed3b-4d91-ae9f-4c0e70160048 · outbound

This paper cites InfoVAE: Information Maximizing Variational Autoencoders.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction InfoVAE: Information Maximizing Variational Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.525056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39b47f2-7bee-48ec-b3b9-6dc07676f185 · outbound

This paper cites We use 8 and 16 layers of ﬂow in case of the densities in Figure 6 and Figure 7 respectively.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction We use 8 and 16 layers of ﬂow in case of the densities in Figure 6 and Figure 7 respectively

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.764475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12a35378-c26b-45c6-ba3d-56a81c1cedfa · outbound

This paper cites tails” between modes), while our conditional non-linear ﬂows does not have distinctive “tails.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction tails” between modes), while our conditional non-linear ﬂows does not have distinctive “tails

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.785054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f151a87e-dec7-4206-ab2f-824786de1e32 · outbound

This paper cites In the middle we show predictions on these samples by the CV AE (with cyclic Kl annealing (Liu et al., 2019)).

Conditional Flow Variational Autoencoders for Structured Sequence Prediction In the middle we show predictions on these samples by the CV AE (with cyclic Kl annealing (Liu et al., 2019))

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.691673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b10cf47-f0cd-4dfb-af7b-58a37a126f3f · outbound

This paper cites tails” or discontinuities and is able to complex capture the multi-modal distributions better. Note, the “ring.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction tails” or discontinuities and is able to complex capture the multi-modal distributions better. Note, the “ring

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.747037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb4d0e40-aaaf-4b3c-9e93-c461d9bf48dc · outbound

This paper cites an unresolved cited work.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:26:52.728424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 003ce25a-a916-4dfb-8172-0648eadfe999 · outbound

This paper cites The attention weighted feature vectors are passed through a ﬁnal fully connected layer to obtain the ﬁnal CNN encoding.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction The attention weighted feature vectors are passed through a ﬁnal fully connected layer to obtain the ﬁnal CNN encoding

Reference 512

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.710479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae2a8bb6-06b9-4295-80c4-8c66f05db419 · outbound

This paper cites Wasserstein Auto-Encoders.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction Wasserstein Auto-Encoders

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.517468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2999f8a7-aeb7-4da8-8a24-b1e02aacec9c · outbound

This paper cites Nachiket Deo and Mohan M Trivedi.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction Nachiket Deo and Mohan M Trivedi

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.821225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0b7d4cf-d8a3-4d78-a235-709686269b14 · outbound

This paper cites DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.496307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffe63da2-8904-4c4d-b845-433a6765f417 · outbound

This paper cites VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.504058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6725353a-0408-4076-84cf-8742099ffbe8 · outbound

This paper cites C ONDITIONAL NON-LINEAR NORMALIZING FLOWS In Subsection 3.1 of the main paper, we describe the inverse operation f−1 i of our non-linear conditional normalizing ﬂows.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction C ONDITIONAL NON-LINEAR NORMALIZING FLOWS In Subsection 3.1 of the main paper, we describe the inverse operation f−1 i of our non-linear conditional normalizing ﬂows

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:26:52.804024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 01e1fbef-c209-424f-b81d-d4f72a4ac723 · inbound

TopoDiffuser: A Diffusion-Based Multimodal Trajectory Prediction Model with Topometric Maps cites this paper.

TopoDiffuser: A Diffusion-Based Multimodal Trajectory Prediction Model with Topometric Maps Conditional Flow Variational Autoencoders for Structured Sequence Prediction

Reference 2020

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T10:15:52.533531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af3c96e1-e250-4944-954d-aa56eb5c8252 · inbound

NAE: Normalizing AutoEncoder cites this paper.

NAE: Normalizing AutoEncoder Conditional Flow Variational Autoencoders for Structured Sequence Prediction

Reference 177

Resolution: unresolved; no resolver link, observed 2026-08-16T00:22:07.956101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.