Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:24:31.103641Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 99 of 99 outbound references and 2 inbound Pith citation observations for arXiv:2507.00425.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:24:31.103641Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T13:11:18.836504Z
99 of 99 outbound references displayed
External citation measurements
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Observation f2fddd25-f0bd-45f9-8951-370a27b3a0d0 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg
Reference 1
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Reference 2
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Reference 3
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Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Dvornek, Sekhar Tatikonda, and James S
Reference 83
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Observation 19e313f9-339c-4bf1-acd3-746fc6c6beef · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Differentiating ycdf = Φ(u t,i) with respect to ut,i yields dycdf dut,i = N(u t,i; 0,1), where N(u t,i; 0,1) is the PDF of the standard normal distribution
Reference 86
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Observation 6035bc44-16c1-4295-ae37-8e21387d3939 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 87
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Observation 1d57d8f7-2173-48d3-9e2a-3e20c38c166f · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 88
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Observation 486e68a3-d494-4ba0-b495-a48a1a3be176 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 89
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Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows For a given component k, z follows a single multivariate Gaussian distribution N(m k, σ2 kId)
Reference 90
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Observation 84c0d40b-d689-4a71-80c7-6235cc1c7586 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows We denote this by α(i) k (z<i) and compute it using Bayes’ rule: P(K=k|z <i) = p(z<i|K=k)P(K=k) p(z<i) = p(z<i|K=k)P(K=k)PV j=1 p(z<i|K=j)P(K=j)
Reference 91
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Observation d5c725e3-6878-4ee7-be37-c645f228c300 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows This corresponds to minimizingKL(q in∥pmodel)
Reference 92
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Observation 8d09be4c-dcc4-4e75-8397-6d1a51f6706d · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Flexible Patch Size
Reference 93
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Observation 8f6282bb-b67a-4d1c-bdb8-b43ccdcfc352 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 94
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Observation e292e8de-8de5-4a3e-8bea-35eb22c245e6 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows ,zt−1)will converge to(µ x1 ,
Reference 95
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Observation 8b85e9ee-8bcb-46d6-8b61-1c8fbaa62efc · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows equation 71) evaluated at zt ≈µ xt will behave as follows: If µk are distinct, then for zt ≈µ xt, Nxt (zt) will be large, while Nj(zt) for j̸=x t will be very small
Reference 96
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Observation 1b6365d5-24f3-4594-a1a7-51bd18d552d8 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows RealNVP [17] built upon this by introducing scaling and shifting operations, thereby increasing model flexibility
Reference 97
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Observation a42cfea9-05fc-4fc0-bf5f-fba8575da275 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Other prominent generative models include Variational Autoencoders (V AEs) [38] and Generative Adversarial Networks (GANs) [23]
Reference 98
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Observation fe394e88-a1d0-4d1f-ae90-a37efe35185c · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows analog bits,
Reference 99
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Observation 525f75c3-7a9a-4953-948d-083700b9c0bb · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 2015
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Observation 74109a62-ed49-49e6-b98c-111d29dd0967 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 2021
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Observation 50485a9d-ab1f-4388-93fd-7043c9f27feb · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 2022
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Observation 164476e6-6f8e-4954-a05f-e9a1575f0201 · outbound
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Unresolved cited work
Reference 2024
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Observation d4e76ec0-99d5-4883-bca9-5014f5d40301 · inbound
Normalizing Flows with Iterative Denoising Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
Reference 23
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Observation 0fc01b74-c007-4d87-9e0d-55c3c894b33e · inbound
From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
Reference 94
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