Paper Citation Record · LEDGER

Exploring Machine Learning and Language Models for Multimodal Depression Detection

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2508.20805.

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

pith.paper-citation-record.v1
2508.20805 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:50:45.174872Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy25
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05dbfbd7-d307-4f7a-af8c-d4e13f1521fa · outbound

This paper cites Depressive disorder (depression),.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Depressive disorder (depression),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.542181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 78f0e45b-fa33-4664-b5c3-c5150aad9d65 · outbound

This paper cites Depression underdiagnosis: Prevalence and associated factors. a population-based study,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Depression underdiagnosis: Prevalence and associated factors. a population-based study,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.533000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5084a4e7-ac63-4273-9683-1732eb732800 · outbound

This paper cites The phq-9: validity of a brief depression severity measure,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection The phq-9: validity of a brief depression severity measure,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.522499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc903cb7-5c1c-463f-8b0c-ff08ffaefb36 · outbound

This paper cites an unresolved cited work.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:50:45.512740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d7fae10a-65de-4984-b908-964ec157828c · outbound

This paper cites Dynamic multimodal measurement of depression severity using deep autoencoding,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Dynamic multimodal measurement of depression severity using deep autoencoding,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.502325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e1b5f62-4996-4eca-8b31-21012174f1bd · outbound

This paper cites D-vlog: Multimodal vlog dataset for depression detection,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection D-vlog: Multimodal vlog dataset for depression detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.491855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c91410c-c525-46b6-9064-c9c386bbfb61 · outbound

This paper cites Automatic depression detection: An emo- tional audio-textual corpus and a gru/bilstm-based model,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Automatic depression detection: An emo- tional audio-textual corpus and a gru/bilstm-based model,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.480868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c13a62c-92f8-412f-8d00-d7d3d7dd66a6 · outbound

This paper cites Semi-structural interview-based chinese multimodal depression corpus towards automatic preliminary screening of depressive disorders,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Semi-structural interview-based chinese multimodal depression corpus towards automatic preliminary screening of depressive disorders,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.469262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3f564301-00e4-41d3-9592-b7a85af4f2a9 · outbound

This paper cites A multi-modal open dataset for mental-disorder analysis,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection A multi-modal open dataset for mental-disorder analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.459277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ac1a04a6-f68e-4b0a-bcc8-12dacf02d2e8 · outbound

This paper cites Amigos: A dataset for affect, personality and mood research on individuals and groups,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Amigos: A dataset for affect, personality and mood research on individuals and groups,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.448946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f86f6bfe-bb3f-4b4b-819f-4e3c91e5157a · outbound

This paper cites Deap: A database for emotion analysis; using physiological signals,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Deap: A database for emotion analysis; using physiological signals,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.438359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8fc6b7a6-3cb4-414a-8037-af0fcdfbd2c6 · outbound

This paper cites Personality and depression: explanatory models and review of the evidence,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Personality and depression: explanatory models and review of the evidence,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.426273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 934870eb-dc8c-49d0-9cf6-b0d56f7dbb72 · outbound

This paper cites Genome- wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Genome- wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.412798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d8025d82-bdd8-4ff2-98e9-d575caccec6b · outbound

This paper cites The First MPDD Challenge: Multimodal Personality-aware Depression Detection.

Exploring Machine Learning and Language Models for Multimodal Depression Detection The First MPDD Challenge: Multimodal Personality-aware Depression Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.115640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afe35dc8-d0db-4bff-8d03-4a1af589a868 · outbound

This paper cites Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.401751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffc1e377-31ce-4822-a447-c3c62bedb23a · outbound

This paper cites Recent developments in opens- mile, the munich open-source multimedia feature extractor,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Recent developments in opens- mile, the munich open-source multimedia feature extractor,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.390370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b82c1d86-5fb3-44e2-bdd9-394087c28940 · outbound

This paper cites Openface 2.0: Facial behavior analysis toolkit,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Openface 2.0: Facial behavior analysis toolkit,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.378108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9609896-5cf8-4c1e-83b2-bbcbf346db14 · outbound

This paper cites Python fer (facial expression recognition),.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Python fer (facial expression recognition),

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.365291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 976c7c93-fbab-4ce8-b0e5-5eb66e4e4d8f · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Xgboost: A scalable tree boosting system,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.131127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04badd7f-f041-4a1d-b7ce-e6e231519a30 · outbound

This paper cites Sup- port vector machines,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Sup- port vector machines,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.345890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e76ffbf5-1d30-40d0-b229-1525d5505557 · outbound

This paper cites Detecting deprefssion on video logs using audiovisual features,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Detecting deprefssion on video logs using audiovisual features,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.335128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 876ee790-4c6d-4e22-b598-7359ad8f4da4 · outbound

This paper cites First transformer-based depression detection using multi-head attention,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection First transformer-based depression detection using multi-head attention,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.324728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9710443-3b8a-4398-b770-2acab3756aed · outbound

This paper cites Late fusion strategies for multimodal depression classi- fication,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Late fusion strategies for multimodal depression classi- fication,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.314433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9978cd25-80c7-482e-9aca-519f78b97f04 · outbound

This paper cites A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention.

Exploring Machine Learning and Language Models for Multimodal Depression Detection A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T14:50:45.224085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6098981b-1962-4860-9196-a0237d76f2cf · outbound

This paper cites Mentalbert: A clinical language model for mental health assessment,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Mentalbert: A clinical language model for mental health assessment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.302349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b13433fe-eee7-4a1f-b3ee-c533a8d8e86d · outbound

This paper cites Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.291221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a56cb99d-013e-4b32-8a00-cbde6c54b550 · outbound

This paper cites The hamilton rating scale for depression,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection The hamilton rating scale for depression,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.279640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 96ed5840-5469-49c8-a287-ca0f8ffc8519 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.269573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16c7d3de-c652-4196-b3f6-2aaf67b80d0d · outbound

This paper cites Densely connected convolutional networks,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Densely connected convolutional networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:50:45.258357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ebb4c977-9d4c-4841-ad6a-ddd0f8681086 · outbound

This paper cites Deep residual learning for image recognition,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Deep residual learning for image recognition,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.165459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05b68c1e-7dc1-404e-a7b2-135670f58e15 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Exploring Machine Learning and Language Models for Multimodal Depression Detection RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.168450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15c8e276-461b-4a01-b5fc-21842f697cd8 · outbound

This paper cites Focal loss for dense object detection,.

Exploring Machine Learning and Language Models for Multimodal Depression Detection Focal loss for dense object detection,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.172045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c9f1d2a-278d-447a-a261-7a82a5284581 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Exploring Machine Learning and Language Models for Multimodal Depression Detection mixup: Beyond Empirical Risk Minimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T14:50:45.174872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.