{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/event-detection/papers/3","list_of":"/task/event-detection","task":"Event Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":10,"rows_per_page":100,"rows":[201,300],"of":940,"counts":{"archive_papers_tagged":940,"with_a_code_link":280,"where_syntology_ran_a_sample":40,"not_listed_spam_title":0,"listed":940,"listed_where_code_ran":40,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":35,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":35,"listed_every_run_a_failure_of_syntologys_instrument":5,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/event-detection","prev":"/task/event-detection/papers/2","next":"/task/event-detection/papers/4","papers":[{"url":"/paper/zero-bias-deep-learning-enabled-quick-and","slug":"zero-bias-deep-learning-enabled-quick-and","title":"Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT","date":"2021-04-08","arxiv_id":"2105.15098","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-learning-for-audio-visual-video","slug":"cross-modal-learning-for-audio-visual-video","title":"Cross-Modal learning for Audio-Visual Video Parsing","date":"2021-04-03","arxiv_id":"2104.04598","repositories_listed":1,"syntology":null},{"url":"/paper/streaming-social-event-detection-and","slug":"streaming-social-event-detection-and","title":"Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks","date":"2021-04-02","arxiv_id":"2104.00853","repositories_listed":1,"syntology":null},{"url":"/paper/bert-prescriptions-to-avoid-unwanted","slug":"bert-prescriptions-to-avoid-unwanted","title":"BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/forward-backward-convolutional-recurrent","slug":"forward-backward-convolutional-recurrent","title":"Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection","date":"2021-03-11","arxiv_id":"2103.06581","repositories_listed":1,"syntology":null},{"url":"/paper/revdet-robust-and-memory-efficient-event","slug":"revdet-robust-and-memory-efficient-event","title":"RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds","date":"2021-03-07","arxiv_id":"2103.04390","repositories_listed":1,"syntology":null},{"url":"/paper/birdnet-a-deep-learning-solution-for-avian","slug":"birdnet-a-deep-learning-solution-for-avian","title":"BirdNET: A deep learning solution for avian diversity monitoring","date":"2021-01-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/msed-a-multi-modal-sleep-event-detection","slug":"msed-a-multi-modal-sleep-event-detection","title":"MSED: a multi-modal sleep event detection model for clinical sleep analysis","date":"2021-01-07","arxiv_id":"2101.02530","repositories_listed":1,"syntology":null},{"url":"/paper/graph-convolutional-networks-for-traffic","slug":"graph-convolutional-networks-for-traffic","title":"Graph Convolutional Networks for traffic anomaly","date":"2020-12-25","arxiv_id":"2012.13637","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-event-detection-with-prototypical","slug":"few-shot-event-detection-with-prototypical","title":"Few-Shot Event Detection with Prototypical Amortized Conditional Random Field","date":"2020-12-04","arxiv_id":"2012.02353","repositories_listed":1,"syntology":null},{"url":"/paper/let-s-hope-it-works-inaccurate-supervision-of","slug":"let-s-hope-it-works-inaccurate-supervision-of","title":"Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing","date":"2020-11-28","arxiv_id":"2011.14101","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-in-video-via-self","slug":"anomaly-detection-in-video-via-self","title":"Anomaly Detection in Video via Self-Supervised and Multi-Task Learning","date":"2020-11-15","arxiv_id":"2011.07491","repositories_listed":1,"syntology":null},{"url":"/paper/seqmix-augmenting-active-sequence-labeling","slug":"seqmix-augmenting-active-sequence-labeling","title":"SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup","date":"2020-10-05","arxiv_id":"2010.02322","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/seqmix-augmenting-active-sequence-labeling#ran","syntology_url":"https://syntology.ai/paper/2010.02322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02322"}},"official":{"repos":["rz-zhang/SeqMix"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/dcasenet-a-joint-pre-trained-deep-neural","slug":"dcasenet-a-joint-pre-trained-deep-neural","title":"DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events","date":"2020-09-21","arxiv_id":"2009.09642","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-neural-event-coreference","slug":"end-to-end-neural-event-coreference","title":"End-to-End Neural Event Coreference Resolution","date":"2020-09-17","arxiv_id":"2009.08153","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-learning-for-interpretable-weakly","slug":"multi-task-learning-for-interpretable-weakly","title":"Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection","date":"2020-08-17","arxiv_id":"2008.07085","repositories_listed":1,"syntology":null},{"url":"/paper/improving-event-detection-via-open-domain","slug":"improving-event-detection-via-open-domain","title":"Improving Event Detection via Open-domain Trigger Knowledge","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tweetscov19-a-knowledge-base-of-semantically","slug":"tweetscov19-a-knowledge-base-of-semantically","title":"TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic","date":"2020-06-25","arxiv_id":"2006.14492","repositories_listed":1,"syntology":null},{"url":"/paper/extensively-matching-for-few-shot-learning","slug":"extensively-matching-for-few-shot-learning","title":"Extensively Matching for Few-shot Learning Event Detection","date":"2020-06-17","arxiv_id":"2006.10093","repositories_listed":1,"syntology":null},{"url":"/paper/red-deep-recurrent-neural-networks-for-sleep","slug":"red-deep-recurrent-neural-networks-for-sleep","title":"RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection","date":"2020-05-15","arxiv_id":"2005.07795","repositories_listed":1,"syntology":null},{"url":"/paper/memory-controlled-sequential-self-attention","slug":"memory-controlled-sequential-self-attention","title":"Memory Controlled Sequential Self Attention for Sound Recognition","date":"2020-05-13","arxiv_id":"2005.06650","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-infant-crying-in-real-world","slug":"classification-of-infant-crying-in-real-world","title":"Infant Crying Detection in Real-World Environments","date":"2020-05-12","arxiv_id":"2005.07036","repositories_listed":1,"syntology":null},{"url":"/paper/maven-a-massive-general-domain-event","slug":"maven-a-massive-general-domain-event","title":"MAVEN: A Massive General Domain Event Detection Dataset","date":"2020-04-28","arxiv_id":"2004.13590","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/maven-a-massive-general-domain-event#ran","syntology_url":"https://syntology.ai/paper/2004.13590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13590"}},"official":{"repos":["THU-KEG/MAVEN-dataset"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-categorization-of-crisis-events-in","slug":"multimodal-categorization-of-crisis-events-in","title":"Multimodal Categorization of Crisis Events in Social Media","date":"2020-04-10","arxiv_id":"2004.04917","repositories_listed":1,"syntology":null},{"url":"/paper/argus-efficient-activity-detection-system-for","slug":"argus-efficient-activity-detection-system-for","title":"Argus: Efficient Activity Detection System for Extended Video Analysis","date":"2020-03-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/event-detection-with-relation-aware-graph","slug":"event-detection-with-relation-aware-graph","title":"Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation","date":"2020-02-25","arxiv_id":"2002.10757","repositories_listed":1,"syntology":null},{"url":"/paper/sound-event-detection-with-depthwise","slug":"sound-event-detection-with-depthwise","title":"Sound Event Detection with Depthwise Separable and Dilated Convolutions","date":"2020-02-02","arxiv_id":"2002.00476","repositories_listed":1,"syntology":null},{"url":"/paper/two-sample-testing-for-event-impacts-in-time","slug":"two-sample-testing-for-event-impacts-in-time","title":"Two-Sample Testing for Event Impacts in Time Series","date":"2020-01-31","arxiv_id":"2001.11930","repositories_listed":1,"syntology":null},{"url":"/paper/eventmapper-detecting-real-world-physical","slug":"eventmapper-detecting-real-world-physical","title":"EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources","date":"2020-01-23","arxiv_id":"2001.08700","repositories_listed":1,"syntology":null},{"url":"/paper/event-detection-with-trigger-aware-lattice","slug":"event-detection-with-trigger-aware-lattice","title":"Event Detection with Trigger-Aware Lattice Neural Network","date":"2019-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sound-event-detection-in-domestic","slug":"sound-event-detection-in-domestic","title":"Sound event detection in domestic environments withweakly labeled data and soundscape synthesis","date":"2019-10-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-with-dynamic-memory-based","slug":"meta-learning-with-dynamic-memory-based","title":"Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection","date":"2019-10-25","arxiv_id":"1910.11621","repositories_listed":1,"syntology":null},{"url":"/paper/musical-instrument-playing-technique","slug":"musical-instrument-playing-technique","title":"Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example","date":"2019-10-20","arxiv_id":"1910.09021","repositories_listed":1,"syntology":null},{"url":"/paper/guided-learning-convolution-system-for-dcase","slug":"guided-learning-convolution-system-for-dcase","title":"Guided Learning Convolution System for DCASE 2019 Task 4","date":"2019-09-11","arxiv_id":"1909.06178","repositories_listed":1,"syntology":null},{"url":"/paper/msnm-s-an-applied-network-monitoring-tool-for","slug":"msnm-s-an-applied-network-monitoring-tool-for","title":"MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and Systems","date":"2019-07-31","arxiv_id":"1907.13612","repositories_listed":1,"syntology":null},{"url":"/paper/city-classification-from-multiple-real-world","slug":"city-classification-from-multiple-real-world","title":"City classification from multiple real-world sound scenes","date":"2019-07-29","arxiv_id":"1905.00979","repositories_listed":1,"syntology":null},{"url":"/paper/language-modelling-for-sound-event-detection","slug":"language-modelling-for-sound-event-detection","title":"Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling","date":"2019-07-19","arxiv_id":"1907.08506","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-discrimination-and-generalization","slug":"distilling-discrimination-and-generalization","title":"Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ai-vs-humans-for-the-diagnosis-of-sleep-apnea","slug":"ai-vs-humans-for-the-diagnosis-of-sleep-apnea","title":"AI vs Humans for the diagnosis of sleep apnea","date":"2019-06-20","arxiv_id":"1906.09936","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-post-processing-algorithms-for","slug":"evaluation-of-post-processing-algorithms-for","title":"Evaluation of post-processing algorithms for polyphonic sound event detection","date":"2019-06-17","arxiv_id":"1906.06909","repositories_listed":1,"syntology":null},{"url":"/paper/cost-sensitive-regularization-for-label","slug":"cost-sensitive-regularization-for-label","title":"Cost-sensitive Regularization for Label Confusion-aware Event Detection","date":"2019-06-14","arxiv_id":"1906.06003","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-event-categorization-with","slug":"fine-grained-event-categorization-with","title":"Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks","date":"2019-06-09","arxiv_id":"1906.04580","repositories_listed":1,"syntology":null},{"url":"/paper/what-you-need-is-a-more-professional-teacher","slug":"what-you-need-is-a-more-professional-teacher","title":"Guided learning for weakly-labeled semi-supervised sound event detection","date":"2019-06-06","arxiv_id":"1906.02517","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-training-for-weakly-supervised","slug":"adversarial-training-for-weakly-supervised","title":"Adversarial Training for Weakly Supervised Event Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sedtwik-segmentation-based-event-detection","slug":"sedtwik-segmentation-based-event-detection","title":"SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vector-valued-graph-trend-filtering-with-non","slug":"vector-valued-graph-trend-filtering-with-non","title":"Vector-Valued Graph Trend Filtering with Non-Convex Penalties","date":"2019-05-29","arxiv_id":"1905.12692","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-feature-for-weakly-supervised","slug":"disentangled-feature-for-weakly-supervised","title":"Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection","date":"2019-05-24","arxiv_id":"1905.10091","repositories_listed":1,"syntology":null},{"url":"/paper/robust-sound-event-detection-in-bioacoustic","slug":"robust-sound-event-detection-in-bioacoustic","title":"Robust sound event detection in bioacoustic sensor networks","date":"2019-05-20","arxiv_id":"1905.08352","repositories_listed":1,"syntology":null},{"url":"/paper/context-awareness-and-embedding-for","slug":"context-awareness-and-embedding-for","title":"Context awareness and embedding for biomedical event extraction","date":"2019-05-02","arxiv_id":"1905.00982","repositories_listed":1,"syntology":null},{"url":"/paper/adversarially-learned-abnormal-trajectory","slug":"adversarially-learned-abnormal-trajectory","title":"Adversarially Learned Abnormal Trajectory Classifier","date":"2019-03-26","arxiv_id":"1903.11040","repositories_listed":1,"syntology":null},{"url":"/paper/sub-event-detection-from-twitter-streams-as-a","slug":"sub-event-detection-from-twitter-streams-as-a","title":"Sub-event detection from Twitter streams as a sequence labeling problem","date":"2019-03-13","arxiv_id":"1903.05396","repositories_listed":1,"syntology":null},{"url":"/paper/dating-documents-using-graph-convolution","slug":"dating-documents-using-graph-convolution","title":"Dating Documents using Graph Convolution Networks","date":"2019-02-01","arxiv_id":"1902.00175","repositories_listed":1,"syntology":null},{"url":"/paper/event-detection-in-twitter-a-keyword-volume","slug":"event-detection-in-twitter-a-keyword-volume","title":"Event detection in Twitter: A keyword volume approach","date":"2019-01-03","arxiv_id":"1901.00570","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-auto-encoders-and-dummy","slug":"object-centric-auto-encoders-and-dummy","title":"Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video","date":"2018-12-11","arxiv_id":"1812.04960","repositories_listed":1,"syntology":{"n":21,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/object-centric-auto-encoders-and-dummy#ran","syntology_url":"https://syntology.ai/paper/1812.04960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04960"}},"official":null}},{"url":"/paper/learning-sound-events-from-webly-labeled-data","slug":"learning-sound-events-from-webly-labeled-data","title":"Learning Sound Events From Webly Labeled Data","date":"2018-11-25","arxiv_id":"1811.09967","repositories_listed":1,"syntology":null},{"url":"/paper/polyphonic-sound-event-detection-by-using","slug":"polyphonic-sound-event-detection-by-using","title":"Polyphonic Sound Event Detection by using Capsule Neural Network","date":"2018-10-15","arxiv_id":"1810.06325","repositories_listed":1,"syntology":null},{"url":"/paper/italian-event-detection-goes-deep-learning","slug":"italian-event-detection-goes-deep-learning","title":"Italian Event Detection Goes Deep Learning","date":"2018-10-04","arxiv_id":"1810.02229","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-contextual-information-via-dynamic","slug":"exploiting-contextual-information-via-dynamic","title":"Exploiting Contextual Information via Dynamic Memory Network for Event Detection","date":"2018-10-03","arxiv_id":"1810.03449","repositories_listed":1,"syntology":null},{"url":"/paper/collective-event-detection-via-a-hierarchical","slug":"collective-event-detection-via-a-hierarchical","title":"Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/open-domain-event-detection-using-distant","slug":"open-domain-event-detection-using-distant","title":"Open-Domain Event Detection using Distant Supervision","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-architecture-to-detect-events","slug":"a-deep-learning-architecture-to-detect-events","title":"A deep learning architecture to detect events in EEG signals during sleep","date":"2018-07-11","arxiv_id":"1807.05981","repositories_listed":1,"syntology":null},{"url":"/paper/self-regulation-employing-a-generative","slug":"self-regulation-employing-a-generative","title":"Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/nugget-proposal-networks-for-chinese-event","slug":"nugget-proposal-networks-for-chinese-event","title":"Nugget Proposal Networks for Chinese Event Detection","date":"2018-05-01","arxiv_id":"1805.00249","repositories_listed":1,"syntology":null},{"url":"/paper/a-closer-look-at-weak-label-learning-for","slug":"a-closer-look-at-weak-label-learning-for","title":"A Closer Look at Weak Label Learning for Audio Events","date":"2018-04-24","arxiv_id":"1804.09288","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-user-geolocation-via-graph","slug":"semi-supervised-user-geolocation-via-graph","title":"Semi-supervised User Geolocation via Graph Convolutional Networks","date":"2018-04-22","arxiv_id":"1804.08049","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semi-supervised-user-geolocation-via-graph#ran","syntology_url":"https://syntology.ai/paper/1804.08049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08049"}},"official":null}},{"url":"/paper/scalable-generalized-dynamic-topic-models","slug":"scalable-generalized-dynamic-topic-models","title":"Scalable Generalized Dynamic Topic Models","date":"2018-03-21","arxiv_id":"1803.07868","repositories_listed":1,"syntology":null},{"url":"/paper/joint-event-detection-and-description-in","slug":"joint-event-detection-and-description-in","title":"Joint Event Detection and Description in Continuous Video Streams","date":"2018-02-28","arxiv_id":"1802.10250","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/joint-event-detection-and-description-in#ran","syntology_url":"https://syntology.ai/paper/1802.10250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10250"}},"official":{"repos":["VisionLearningGroup/JEDDi-Net"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/detecting-volcano-deformation-in-insar-using","slug":"detecting-volcano-deformation-in-insar-using","title":"Detecting Volcano Deformation in InSAR using Deep learning","date":"2018-01-21","arxiv_id":"1803.00380","repositories_listed":1,"syntology":null},{"url":"/paper/convsccs-convolutional-self-controlled-case","slug":"convsccs-convolutional-self-controlled-case","title":"ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection","date":"2017-12-21","arxiv_id":"1712.08243","repositories_listed":1,"syntology":null},{"url":"/paper/event-radar-real-time-local-event-detection","slug":"event-radar-real-time-local-event-detection","title":"Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams","date":"2017-08-19","arxiv_id":"1708.05878","repositories_listed":1,"syntology":null},{"url":"/paper/abnormal-event-detection-on-bmtt-pets-2017","slug":"abnormal-event-detection-on-bmtt-pets-2017","title":"Abnormal event detection on BMTT-PETS 2017 surveillance challenge","date":"2017-07-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sound-event-detection-using-spatial-features","slug":"sound-event-detection-using-spatial-features","title":"Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network","date":"2017-06-07","arxiv_id":"1706.02291","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-recurrent-neural-networks-for-6","slug":"convolutional-recurrent-neural-networks-for-6","title":"Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection","date":"2017-02-21","arxiv_id":"1702.06286","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-study-of-drone-sound-detection-in","slug":"empirical-study-of-drone-sound-detection-in","title":"Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks","date":"2017-01-20","arxiv_id":"1701.05779","repositories_listed":1,"syntology":null},{"url":"/paper/aenet-learning-deep-audio-features-for-video","slug":"aenet-learning-deep-audio-features-for-video","title":"AENet: Learning Deep Audio Features for Video Analysis","date":"2017-01-03","arxiv_id":"1701.00599","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-very-short-texts","slug":"representation-learning-for-very-short-texts","title":"Representation learning for very short texts using weighted word embedding aggregation","date":"2016-07-02","arxiv_id":"1607.00570","repositories_listed":1,"syntology":null},{"url":"/paper/bidirectional-recurrent-neural-networks-for","slug":"bidirectional-recurrent-neural-networks-for","title":"Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records","date":"2016-06-25","arxiv_id":"1606.07953","repositories_listed":1,"syntology":null},{"url":"/paper/flica-a-framework-for-leader-identification","slug":"flica-a-framework-for-leader-identification","title":"Coordination Event Detection and Initiator Identification in Time Series Data","date":"2016-03-04","arxiv_id":"1603.01570","repositories_listed":1,"syntology":null},{"url":"/paper/event-detection-and-domain-adaptation-with","slug":"event-detection-and-domain-adaptation-with","title":"Event Detection and Domain Adaptation with Convolutional Neural Networks","date":"2015-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/eigenevent-an-algorithm-for-event-detection","slug":"eigenevent-an-algorithm-for-event-detection","title":"EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance","date":"2014-06-13","arxiv_id":"1406.3496","repositories_listed":1,"syntology":null},{"url":null,"slug":"frequency-dynamic-convolutions-for-sound","title":"Frequency Dynamic Convolutions for Sound Event Detection","date":"2025-06-15","arxiv_id":"2506.12785","repositories_listed":0,"syntology":null},{"url":null,"slug":"dicore-enhancing-zero-shot-event-detection","title":"DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning","date":"2025-06-05","arxiv_id":"2506.05128","repositories_listed":0,"syntology":null},{"url":null,"slug":"diamond-an-llm-driven-agent-for-context-aware","title":"DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization","date":"2025-06-03","arxiv_id":"2506.02351","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-real-time-assessment-of-infrasound","title":"Towards real-time assessment of infrasound event detection capability using deep learning-based transmission loss estimation","date":"2025-06-03","arxiv_id":"2506.06358","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-potential-of-ssl-models-for","title":"Exploring the Potential of SSL Models for Sound Event Detection","date":"2025-05-17","arxiv_id":"2505.11889","repositories_listed":0,"syntology":null},{"url":null,"slug":"gameplay-highlights-generation","title":"Gameplay Highlights Generation","date":"2025-05-12","arxiv_id":"2505.07721","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-spiking-vision-transformer-for-object","title":"Hybrid Spiking Vision Transformer for Object Detection with Event Cameras","date":"2025-05-12","arxiv_id":"2505.07715","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-spotting-and-precise-event-detection","title":"Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges","date":"2025-05-06","arxiv_id":"2505.03991","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-pca-based-outlier-detection-for","title":"Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions","date":"2025-04-22","arxiv_id":"2504.15846","repositories_listed":0,"syntology":null},{"url":null,"slug":"cst-former-multidimensional-attention-based","title":"CST-former: Multidimensional Attention-based Transformer for Sound Event Localization and Detection in Real Scenes","date":"2025-04-17","arxiv_id":"2504.12870","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-operating-room-workflow","title":"Privacy-Preserving Operating Room Workflow Analysis using Digital Twins","date":"2025-04-17","arxiv_id":"2504.12552","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-object-and-event-detection-service","title":"Real-time Object and Event Detection Service through Computer Vision and Edge Computing","date":"2025-04-15","arxiv_id":"2504.11662","repositories_listed":0,"syntology":null},{"url":null,"slug":"petnet-coincident-particle-event-detection","title":"PETNet -- Coincident Particle Event Detection using Spiking Neural Networks","date":"2025-04-09","arxiv_id":"2504.06730","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-solid-state-nanopore-signal-generator-for","title":"A Solid-State Nanopore Signal Generator for Training Machine Learning Models","date":"2025-04-07","arxiv_id":"2504.05466","repositories_listed":0,"syntology":null},{"url":null,"slug":"formula-supervised-sound-event-detection-pre","title":"Formula-Supervised Sound Event Detection: Pre-Training Without Real Data","date":"2025-04-06","arxiv_id":"2504.04428","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-mixed-features-for-imbalance-data","title":"Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring","date":"2025-03-26","arxiv_id":"2503.22730","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-knowledge-graphs-and-llms-for","title":"Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging","date":"2025-03-12","arxiv_id":"2503.13499","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-regression-for-leitmotif-detection","title":"Boundary Regression for Leitmotif Detection in Music Audio","date":"2025-03-11","arxiv_id":"2503.07977","repositories_listed":0,"syntology":null},{"url":null,"slug":"fintmmbench-benchmarking-temporal-aware-multi","title":"FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance","date":"2025-03-07","arxiv_id":"2503.05185","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-in-biomechanics-key","title":"Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements","date":"2025-03-05","arxiv_id":"2503.03717","repositories_listed":0,"syntology":null}],"record_sha256":"c0103ac33822105253d034e9c1bcacf6b3b568c604d7285764f379d14d8843ce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}