Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning | Paper Citation Record

Observation 320035ec-a2e5-43fc-8369-8613e71f1d25 · outbound

This paper cites Latent dirichlet allocation,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Latent dirichlet allocation,

Reference 1

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Observation e9dc5a25-ddf1-448e-a973-51ebd1973630 · outbound

This paper cites Survival topic models for predict- ing outcomes for trauma patients,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Survival topic models for predict- ing outcomes for trauma patients,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.769736Z

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Observation 25cd579f-6186-4ff5-a128-6b63f79d2b9e · outbound

This paper cites Source-lda: Enhancing probabilistic topic models using prior knowledge sources,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Source-lda: Enhancing probabilistic topic models using prior knowledge sources,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.600641Z

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Observation 89c6a46d-1c9f-4899-8cdf-f79f013aa4c9 · outbound

This paper cites Is automated topic model evaluation broken? the incoherence of coherence,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Is automated topic model evaluation broken? the incoherence of coherence,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.567659Z

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Observation adc8aa28-21d5-4b2b-9a19-66623f183224 · outbound

This paper cites Latent dirichlet allocation (lda) and topic modeling: models, applications, a survey,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Latent dirichlet allocation (lda) and topic modeling: models, applications, a survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.548810Z

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Observation 389c2ad5-f5c5-4cba-8422-c1571b72c64f · outbound

This paper cites Tensor topic models with graphs and applications on individ- ualized travel patterns,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Tensor topic models with graphs and applications on individ- ualized travel patterns,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.532767Z

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Observation 472c9991-5490-4ba2-aa79-7a6a12853128 · outbound

This paper cites Intent mining: A social and semantic enhanced topic model for operation-friendly digital marketing,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Intent mining: A social and semantic enhanced topic model for operation-friendly digital marketing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.439374Z

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Observation 881318f0-b4c1-4083-bcc3-738b29ed910a · outbound

This paper cites Neural variational inference for text processing,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Neural variational inference for text processing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.343726Z

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Observation dc925fd3-0d50-40ce-ad1e-9ba7a913e7ad · outbound

This paper cites Autoencoding variational inference for topic models,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Autoencoding variational inference for topic models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.227889Z

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Observation 1f463308-99bb-4627-997c-89904f0089bc · outbound

This paper cites Auto-Encoding Variational Bayes.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Auto-Encoding Variational Bayes

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:21.642484Z

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Observation acf321e0-62b2-4b85-8630-834fb5982533 · outbound

This paper cites Topic modeling in embedding spaces,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Topic modeling in embedding spaces,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:21.647043Z

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Observation 9d3cc634-5e70-4510-86fb-ff2a6708d646 · outbound

This paper cites Neural Topic Model via Optimal Transport.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Neural Topic Model via Optimal Transport

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:21.653754Z

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Observation 1d240f17-34dc-4c1c-87dd-0dc4267cb152 · outbound

This paper cites Coherence-aware neural topic modeling,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Coherence-aware neural topic modeling,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.145048Z

Source-reported events for the cited work

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Observation 3c607fe2-7186-4dca-a15d-19d6650d9a59 · outbound

This paper cites Topic model or topic twaddle? re- evaluating semantic interpretability measures,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Topic model or topic twaddle? re- evaluating semantic interpretability measures,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.129164Z

Source-reported events for the cited work

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Observation 64929be6-40b2-4307-9108-62ed50108214 · outbound

This paper cites Topic modeling with wasserstein autoencoders,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Topic modeling with wasserstein autoencoders,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.116114Z

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Observation e11d207c-3d2b-41ae-a880-355fe9d39b0f · outbound

This paper cites Reading tea leaves: How humans interpret topic models,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Reading tea leaves: How humans interpret topic models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:23.041302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ee2fc700-2953-46ac-8eb8-809a61e1ce4c · outbound

This paper cites Discriminative topic mining via category-name guided text embedding,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Discriminative topic mining via category-name guided text embedding,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.945420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b9a7fd96-39e5-432b-87cf-114626656f0c · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Categorical Reparameterization with Gumbel-Softmax

Reference 18

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Observation 34ac6da7-f60f-40fa-86d8-5bdf505a263c · outbound

This paper cites Whai: Weibull hybrid autoencoding inference for deep topic modeling,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Whai: Weibull hybrid autoencoding inference for deep topic modeling,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.820093Z

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Observation b3c7842f-6607-4adf-9914-05092c3cd3d5 · outbound

This paper cites Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.769852Z

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Observation 0a94bf60-1b84-43b4-8041-a46b7105fd9d · outbound

This paper cites Atm: Adversarial-neural topic model,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Atm: Adversarial-neural topic model,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.757460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e0e2331d-1d18-4937-9332-c029ce9ccfc0 · outbound

This paper cites Neural topic model with attention for supervised learning,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Neural topic model with attention for supervised learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.738537Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 675e393c-f528-4f4b-9067-ab084d964e4e · outbound

This paper cites Graph attention topic modeling network,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Graph attention topic modeling network,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.721915Z

Source-reported events for the cited work

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Observation a4c84015-cafe-48fb-82fe-f49b4a4cb485 · outbound

This paper cites Graph topic neural network for document representation,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Graph topic neural network for document representation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.650710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4418290e-65ae-4e63-8f8a-a35225587236 · outbound

This paper cites Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:22.008712Z

Source-reported events for the cited work

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Observation 25c75594-3c3a-4597-a040-3918a6907d09 · outbound

This paper cites Effective neural topic modeling with embedding clustering regularization,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Effective neural topic modeling with embedding clustering regularization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.552299Z

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Observation 06f08d4f-47df-496c-b523-e42f2e62e752 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Learning deep representations by mutual information estimation and maximization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:21.725364Z

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Observation 6b7fc61c-7c6f-4823-b621-8158b111ba0c · outbound

This paper cites Supervised contrastive learning,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Supervised contrastive learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.460423Z

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Observation a5d5cf96-687e-4285-8dc3-22caa3b5c497 · outbound

This paper cites Detco: Unsupervised contrastive learning for object detection,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Detco: Unsupervised contrastive learning for object detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.393672Z

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Observation 77b2ee38-f95e-4f96-ba7a-299da677a4c6 · outbound

This paper cites Fsce: Few-shot object detection via contrastive proposal encoding,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Fsce: Few-shot object detection via contrastive proposal encoding,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.378197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29539def-0ace-4b90-b2c4-d4eea7f2589b · outbound

This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Virtual adversarial training: a regularization method for supervised and semi-supervised learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.365273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3e8251fb-ce0d-4fc7-98ef-607c65cbdf3c · outbound

This paper cites An efficient framework for learning sentence representations,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning An efficient framework for learning sentence representations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.353750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 575dd711-4c15-422a-8788-60f5296850ff · outbound

This paper cites Improving topic disentanglement via contrastive learning,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Improving topic disentanglement via contrastive learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.340309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5cddc6ec-694b-4a23-96ab-562836901123 · outbound

This paper cites Contrastive learning for neural topic model,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Contrastive learning for neural topic model,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.329106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dd1acca9-ffba-481f-9baf-00c81507c9d3 · outbound

This paper cites Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:21.973371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b9103167-cfa1-46c0-9ce3-8210c93f1992 · outbound

This paper cites Keyword assisted em- bedded topic model,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Keyword assisted em- bedded topic model,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.257693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 500c39a7-24b5-4071-9e9f-002906d83d81 · outbound

This paper cites Enhancing neural topic model with multi-level supervisions from seed words,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Enhancing neural topic model with multi-level supervisions from seed words,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.214935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fec60fb1-4895-4fb1-94b8-4ad54436bcbb · outbound

This paper cites Neural topic model with reinforcement learning,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Neural topic model with reinforcement learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.171939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1775e33-90d6-459b-a04e-7a9a59646a8e · outbound

This paper cites Are Neural Topic Models Broken?.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Are Neural Topic Models Broken?

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:21.953568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 26ab6c0a-1dd7-4593-9309-16945ffca720 · outbound

This paper cites Large-scale correlation analysis of automated metrics for topic models,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Large-scale correlation analysis of automated metrics for topic models,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.150655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85a74f23-0168-4374-907b-b27aca9cad12 · outbound

This paper cites Reparameterizable Subset Sampling via Continuous Relaxations.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Reparameterizable Subset Sampling via Continuous Relaxations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:21.785109Z

Source-reported events for the cited work

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Observation f92e24f0-ede1-4ba7-af9d-a0ae727e7942 · outbound

This paper cites Stochastic optimization of areas under precision-recall curves with provable convergence,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Stochastic optimization of areas under precision-recall curves with provable convergence,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.139211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9ebce470-4ddd-437e-bf81-1325dedb47a3 · outbound

This paper cites Stochastic AUC Maximization with Deep Neural Networks.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Stochastic AUC Maximization with Deep Neural Networks

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:21.913880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fa12d50c-0dab-4636-a4c6-3f60ae12b1b5 · outbound

This paper cites Mutual information neural estimation,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Mutual information neural estimation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.126185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ee87b1b0-dc58-44e2-89a0-673d53cc2f85 · outbound

This paper cites Newsweeder: Learning to filter netnews,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Newsweeder: Learning to filter netnews,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.112732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3914e99c-6861-4cee-bc01-900064b0e0d4 · outbound

This paper cites Importance of semantic representation: Dataless classification.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Importance of semantic representation: Dataless classification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.100163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec00a57a-40f1-4124-bbe8-c45ab06a11fb · outbound

This paper cites Short text topic modeling with topic distribution quantization and negative sampling decoder,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Short text topic modeling with topic distribution quantization and negative sampling decoder,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.088238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bdf07c56-b984-4d58-ac70-6183ee046dd4 · outbound

This paper cites Keyword Assisted Embedded Topic Model.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Keyword Assisted Embedded Topic Model

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:21.892118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1508c6b0-c2e9-4e89-9f9d-92ed85063f35 · outbound

This paper cites Revisiting Automated Topic Model Evaluation with Large Language Models.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Revisiting Automated Topic Model Evaluation with Large Language Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:21.870770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06901e58-7fc9-448a-8223-2c3d759dfa57 · outbound

This paper cites On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.074616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0daf10dc-29b5-425e-b5ad-5b55435e1732 · outbound

This paper cites On-line trend analysis with topic models:# twitter trends detection topic model online,.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning On-line trend analysis with topic models:# twitter trends detection topic model online,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:22.062106Z

Source-reported events for the cited work

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