GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry | Paper Citation Record

Observation da0108b7-b71c-4b9d-82d2-70d947483212 · outbound

This paper cites TDAM: A Topic-Dependent Attention Model for Sentiment Analysis.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry TDAM: A Topic-Dependent Attention Model for Sentiment Analysis

Reference 1

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Observation 3a3b5bbb-68e9-42b5-baf6-2fa5686c8fcb · outbound

This paper cites Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T10:06:23.920014Z

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Observation 9d69335a-d167-4858-8ea0-2ec366c6e1bc · outbound

This paper cites Overview of the HASOC Track at FIRE 2024: Hate-Speech Identification in English and Bengali.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Overview of the HASOC Track at FIRE 2024: Hate-Speech Identification in English and Bengali

Reference 4

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Observation 67a517d5-0122-4d8e-af1d-1342d62a63bd · outbound

This paper cites TOPIC MAP-BN: SCALABLE AND EXPLAINABLE FRAME- WORK FOR CROSS-SOURCE BANGLA NEWS RECOMMENDATION WITH BANGLABERT AND BERTOPIC.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry TOPIC MAP-BN: SCALABLE AND EXPLAINABLE FRAME- WORK FOR CROSS-SOURCE BANGLA NEWS RECOMMENDATION WITH BANGLABERT AND BERTOPIC

Reference 5

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Observation 5b4806e1-6447-4057-ae97-263c405bf03c · outbound

This paper cites Latent Dirichlet Allocation.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Latent Dirichlet Allocation

Reference 6

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Observation 07c6c7eb-8a75-44cf-bb83-6d9466084000 · outbound

This paper cites Learning the parts of objects by non-negative matrix factorization.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Learning the parts of objects by non-negative matrix factorization

Reference 7

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Observation 764407a4-657f-4f35-97fd-99938e742538 · outbound

This paper cites Autoencoding Variational Inference For Topic Models.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Autoencoding Variational Inference For Topic Models

Reference 8

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Observation eb18d4bc-26ac-473a-8550-cca145c968cb · outbound

This paper cites Topic modeling in embedding spaces.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Topic modeling in embedding spaces

Reference 9

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Observation e59ea3e7-3c84-449d-9ed7-72bcb9e57a1c · outbound

This paper cites Pre-trainingisaHotTopic: ContextualizedDocumentEmbeddingsImprove Topic Coherence.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Pre-trainingisaHotTopic: ContextualizedDocumentEmbeddingsImprove Topic Coherence

Reference 10

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Observation d7bf4de2-68b6-4ffb-b2d8-2d70571db9ed · outbound

This paper cites Cross-lingual Contextualized Topic Models with Zero- shot Learning.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Cross-lingual Contextualized Topic Models with Zero- shot Learning

Reference 11

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Observation 74a98a77-5248-4a9f-921a-c69ea169f654 · outbound

This paper cites Top2Vec: Distributed Representations of Topics.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Top2Vec: Distributed Representations of Topics

Reference 12

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Observation a59345d9-39ea-4fdc-9eb3-e9187db90d1c · outbound

This paper cites BERTopic: Neural topic modeling with a class-based TF-IDF procedure.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry BERTopic: Neural topic modeling with a class-based TF-IDF procedure

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.571272Z

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Observation 9cc57c44-4f75-4616-a7c0-cced4b01e102 · outbound

This paper cites GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model

Reference 14

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Observation 603ebc2e-2056-432d-82a1-6d6e6c62bfaa · outbound

This paper cites Graph Contrastive Topic Model.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Graph Contrastive Topic Model

Reference 15

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Observation b3197f1a-7cd9-4638-aa26-76fc89ea2190 · outbound

This paper cites GINopic: Topic Modeling with Graph Isomorphism Network.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry GINopic: Topic Modeling with Graph Isomorphism Network

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:06:26.185966Z

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Observation 7c2b5663-0b12-483f-9e1b-c6c3b92aec2d · outbound

This paper cites Graph2topic: An opensource topic modeling framework based on sentence embedding and community detection.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Graph2topic: An opensource topic modeling framework based on sentence embedding and community detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.966959Z

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Observation 195b343c-ef03-47e6-8262-f666341b4588 · outbound

This paper cites Topic Modeling Revisited: A Document Graph-based Neural Network Perspective.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.957216Z

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Observation f341d1e2-981d-4752-9dd0-b6855dbded1f · outbound

This paper cites TopicGPT: A Prompt-based Topic Modeling Framework.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry TopicGPT: A Prompt-based Topic Modeling Framework

Reference 19

Resolution: malformed identifier; no resolver link, observed 2026-08-06T10:06:23.594703Z

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Observation d0f7ad73-a945-4662-8a83-7db2f893deb6 · outbound

This paper cites Ethnologue: Languages of the World – Bengali; 2025.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Ethnologue: Languages of the World – Bengali; 2025

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.945018Z

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Observation 6ea03859-23b3-40ac-b0b7-857bc2236474 · outbound

This paper cites Topic Modelling in Bangla Language: An LDA Approach to Optimize Topics and News Classification.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Topic Modelling in Bangla Language: An LDA Approach to Optimize Topics and News Classification

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-06T10:06:26.172383Z

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Observation 19603456-42a5-4e7a-bdd5-be28b96636b2 · outbound

This paper cites LDA2Vec: Combining LDA and Word2Vec for Topic Mod- eling in Bangla.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry LDA2Vec: Combining LDA and Word2Vec for Topic Mod- eling in Bangla

Reference 22

Resolution: verified exact; doi, observed 2026-08-06T10:06:23.884027Z

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Observation 439ef77f-e955-4c17-b686-b501d14bd4df · outbound

This paper cites Topic Modeling and Trend Analysis of Bengali News Articles.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Topic Modeling and Trend Analysis of Bengali News Articles

Reference 23

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Observation df2d4b89-b780-4781-a324-35ccbca67161 · outbound

This paper cites Combining BERT with LDA: Improved Topic Mod- eling in Bengali Language.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Combining BERT with LDA: Improved Topic Mod- eling in Bengali Language

Reference 24

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Observation f48312ec-25b4-4c3c-a8d6-7c82e3503f07 · outbound

This paper cites Likelihood Corpus Distribution: A Dirichlet-Polynomial Clustering Model for Bengali Topic Modeling.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Likelihood Corpus Distribution: A Dirichlet-Polynomial Clustering Model for Bengali Topic Modeling

Reference 25

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Observation f44087b7-0abd-4d02-add6-87fbfabc8ae2 · outbound

This paper cites Clustering LLM-based Word Embeddings to Determine Topics from Bangla Articles.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Clustering LLM-based Word Embeddings to Determine Topics from Bangla Articles

Reference 26

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Observation 2ffa7e37-a1b8-447d-ae69-cebf302b821a · outbound

This paper cites Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:06:25.836723Z

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Observation 4987454c-d4be-4662-ab9d-a1b14f30757b · outbound

This paper cites Shironaam: Bengali News Headline Generation using Auxiliary Information.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Shironaam: Bengali News Headline Generation using Auxiliary Information

Reference 28

Resolution: verified exact; doi, observed 2026-08-06T10:06:23.863919Z

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Observation 0ba557a5-8233-4b1e-a233-13565b83a622 · outbound

This paper cites Bangla News Article Dataset (BNAD): A Standard Repository of 1.9 Million News Articles from Nine Bangla News Websites.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Bangla News Article Dataset (BNAD): A Standard Repository of 1.9 Million News Articles from Nine Bangla News Websites

Reference 29

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Observation 4f76a135-6a71-4435-9202-ede3bdeee0d8 · outbound

This paper cites BanFakeNews: A Dataset for Detecting Fake News in Bangla.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry BanFakeNews: A Dataset for Detecting Fake News in Bangla

Reference 30

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Observation b84b5cb0-7691-41b8-94c8-39e723193a8b · outbound

This paper cites GloVe: Global Vectors for Word Representation.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry GloVe: Global Vectors for Word Representation

Reference 31

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Observation 4cc83fb1-1e3f-465c-8c2a-9babf8ed9a37 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Semi-Supervised Classification with Graph Convolutional Networks

Reference 32

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Observation 635ab079-a2b0-4729-9ab9-a8d540b64102 · outbound

This paper cites Indexing by latent semantic analysis.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Indexing by latent semantic analysis

Reference 33

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This paper cites Neuralvariationalinferenceandlearninginbeliefnetworks.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Neuralvariationalinferenceandlearninginbeliefnetworks

Reference 34

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Observation 63dc61e9-9bac-45cb-9f9b-1e3eb0b1be14 · outbound

This paper cites Auto-encoding variational bayes.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Auto-encoding variational bayes

Reference 35

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Observation 15841cb3-8a5c-449d-abb9-0407ddbf261b · outbound

This paper cites CluWords: exploiting semantic word clustering representation for enhanced topic modeling.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry CluWords: exploiting semantic word clustering representation for enhanced topic modeling

Reference 36

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Observation 44a02d49-ebab-4af1-9a4b-6f297898b859 · outbound

This paper cites Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 37

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Observation aa5be899-57cd-4e43-8d9b-b25da2b19236 · outbound

This paper cites Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

Reference 38

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Observation b9da88f4-90a0-4ea3-815e-546efc72d0f8 · outbound

This paper cites Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

Reference 39

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This paper cites A Systematic Literature Review on English and Bangla Topic Modeling.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry A Systematic Literature Review on English and Bangla Topic Modeling

Reference 40

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Observation 10f40fdb-2cfa-4780-b91f-45f8fd40b113 · outbound

This paper cites Bangla-BERT: Transformer- Based Eﬀicient Model for Transfer Learning and Language Understanding.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Bangla-BERT: Transformer- Based Eﬀicient Model for Transfer Learning and Language Understanding

Reference 41

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Observation 9a035190-5373-405a-b067-a738a11faa34 · outbound

This paper cites Support vector machines and Word2vec for text classification with semantic features.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Support vector machines and Word2vec for text classification with semantic features

Reference 42

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:25.291157Z

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Observation 19b3c792-07f0-47a8-8c59-c3e6ed6b9db0 · outbound

This paper cites Measuring document similarity with weighted averages of word embeddings.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Measuring document similarity with weighted averages of word embeddings

Reference 43

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:25.115966Z

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Observation c8ad5383-b869-408d-b509-2bc40dbfe0e6 · outbound

This paper cites Improving a tf-idf weighted document vector embedding.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Improving a tf-idf weighted document vector embedding

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:06:24.966374Z

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Observation c0d2ade5-037c-46c1-87b1-fc0c9c5c1fe5 · outbound

This paper cites Graph Convolutional Networks for Text Classification.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Graph Convolutional Networks for Text Classification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.865202Z

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Observation 3f98fef0-3a49-4e1a-b4a1-ad517e34fdc5 · outbound

This paper cites Graph Neural Networks: A Review of Methods and Applications.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Graph Neural Networks: A Review of Methods and Applications

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.855653Z

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Observation 2cf72041-aaa3-4456-8bc6-e04e67057668 · outbound

This paper cites A Comprehensive Survey on Graph Neural Networks.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry A Comprehensive Survey on Graph Neural Networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.846059Z

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Observation 71c6d87c-bf29-435d-9146-4ad0e41adf1d · outbound

This paper cites Deep Graph Contrastive Representation Learning.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Deep Graph Contrastive Representation Learning

Reference 48

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:24.952229Z

Source-reported events for the cited work

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Observation 5ccc165f-cfbf-434f-9463-541d633c499e · outbound

This paper cites Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.836332Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f7fd3b3-3f6a-44a1-a482-6cf692ae3f17 · outbound

This paper cites Hybrid Margin Contrastive Loss for Graph Neural Networks.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Hybrid Margin Contrastive Loss for Graph Neural Networks

Reference 50

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:26.556863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89dac003-fcd0-4f49-a5dd-1a07c5b86c61 · outbound

This paper cites Cluster-GCN: An Eﬀicient Algorithm for Training Deep and Large Graph Convolutional Networks.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Cluster-GCN: An Eﬀicient Algorithm for Training Deep and Large Graph Convolutional Networks

Reference 51

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:24.644646Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5fa60b1d-3c40-43a2-9c44-5e7c8ecefb01 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry A Simple Framework for Contrastive Learning of Visual Representations

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.826555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3dc9c3dd-dc66-4036-bd3c-bc7f6700d20d · outbound

This paper cites Bangla SBERT - Sentence Embedding Using Multilingual Knowledge Distillation.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Bangla SBERT - Sentence Embedding Using Multilingual Knowledge Distillation

Reference 53

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:24.454750Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eca37dd0-9054-416f-8840-b62cf9dbaf92 · outbound

This paper cites Visualizing data using t-SNE.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Visualizing data using t-SNE

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.817545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55587a06-1595-4773-ad09-1a7501ab5d52 · outbound

This paper cites The Psycho-Biology of Language: An Introduction to Dynamic Philology.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry The Psycho-Biology of Language: An Introduction to Dynamic Philology

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.808352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2bf13f1-fc78-45d7-b10d-5b8be19f9d5b · outbound

This paper cites Lexical Diversity and Language Development.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Lexical Diversity and Language Development

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.798889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35e91c83-b3f5-45a7-a8fa-00dfb587b351 · outbound

This paper cites MTLD, vocd-D, and HD-D: A Validation Study of Sophisticated Approaches to Lexical Diversity Assessment.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry MTLD, vocd-D, and HD-D: A Validation Study of Sophisticated Approaches to Lexical Diversity Assessment

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.722719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3810551b-21b6-44f8-bf6a-ffcfe3f3ecd8 · outbound

This paper cites Cutting the Gordian Knot: The Moving-Average Type–Token Ratio.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Cutting the Gordian Knot: The Moving-Average Type–Token Ratio

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.726085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b9a270-e50a-485f-8599-a4cf089f8486 · outbound

This paper cites Psychometric Evaluation of Lexical Diversity Indices: As- sessing Length Effects.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Psychometric Evaluation of Lexical Diversity Indices: As- sessing Length Effects

Reference 59

Resolution: verified exact; doi, observed 2026-08-06T10:06:23.815897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 516126cc-1bd7-4c53-be6e-8dfa34e1b0be · outbound

This paper cites Language and Thought.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Language and Thought

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.789656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 92aa5f48-db5c-40ac-994a-16e544ee1609 · outbound

This paper cites An Assessment of the Range and Usefulness of Lexical Diversity Measures and the Potential of the Measure of Textual Lexical Diversity (MTLD) [Ph.D.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry An Assessment of the Range and Usefulness of Lexical Diversity Measures and the Potential of the Measure of Textual Lexical Diversity (MTLD) [Ph.D

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.779375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 67a88241-203b-4b4f-95ec-7e75715ea8fa · outbound

This paper cites Twenty Newsgroups; 1997.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Twenty Newsgroups; 1997

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.740452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ed7a96e-f338-4d2a-85d2-943e03e2a30c · outbound

This paper cites Automatic Evaluation of Topic Coherence.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Automatic Evaluation of Topic Coherence

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.769351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e752821-d96c-47cb-8c02-33f6775ecda9 · outbound

This paper cites Exploring the Space of Topic Coherence Measures.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Exploring the Space of Topic Coherence Measures

Reference 64

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:24.288269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6802c2da-c123-45aa-8af5-fe23aa024c5f · outbound

This paper cites A Similarity Measure for Indefinite Rankings.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry A Similarity Measure for Indefinite Rankings

Reference 65

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T10:06:24.088898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 92663592-39f1-4976-b444-23995b488fc7 · outbound

This paper cites Software Framework for Topic Modelling with Large Corpora.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Software Framework for Topic Modelling with Large Corpora

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.759201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5967b694-913f-4732-88ae-0547c4fb37d4 · outbound

This paper cites an unresolved cited work.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-06T10:06:26.749489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1e26db07-bdae-48e8-9d83-5ddc220eb15a · outbound

This paper cites Convex and semi-nonnegative matrix factorizations.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Convex and semi-nonnegative matrix factorizations

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.760037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c122f9ee-cd42-42c0-a3b5-bb39cd391dd3 · outbound

This paper cites Statistical Comparisons of Classifiers over Multiple Data Sets.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Statistical Comparisons of Classifiers over Multiple Data Sets

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:26.740150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

Outbound references

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