{"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/time-series/papers/19","list_of":"/task/time-series","task":"Time Series Analysis","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":19,"pages_in_order":68,"rows_per_page":100,"rows":[1801,1900],"of":6748,"counts":{"archive_papers_tagged":6748,"with_a_code_link":1993,"where_syntology_ran_a_sample":383,"not_listed_spam_title":0,"listed":6748,"listed_where_code_ran":383,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":327,"every_run_a_failure_of_syntologys_instrument":56,"listed_with_a_run_with_no_instrument_failure":327,"listed_every_run_a_failure_of_syntologys_instrument":56,"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/time-series","prev":"/task/time-series/papers/18","next":"/task/time-series/papers/20","papers":[{"url":"/paper/adversarial-generation-of-time-frequency","slug":"adversarial-generation-of-time-frequency","title":"Adversarial Generation of Time-Frequency Features with application in audio synthesis","date":"2019-02-11","arxiv_id":"1902.04072","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-recurrent-neural-network-memory","slug":"investigating-recurrent-neural-network-memory","title":"Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution","date":"2019-02-06","arxiv_id":"1902.02390","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-scalable-representation-learning","slug":"unsupervised-scalable-representation-learning","title":"Unsupervised Scalable Representation Learning for Multivariate Time Series","date":"2019-01-30","arxiv_id":"1901.10738","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-scalable-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1901.10738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10738"}},"official":{"repos":["White-Link/UnsupervisedScalableRepresentationLearningTimeSeries"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/partially-exchangeable-networks-and","slug":"partially-exchangeable-networks-and","title":"Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation","date":"2019-01-29","arxiv_id":"1901.10230","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-filters-learning-independent","slug":"recurrent-neural-filters-learning-independent","title":"Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction","date":"2019-01-23","arxiv_id":"1901.08096","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/recurrent-neural-filters-learning-independent#ran","syntology_url":"https://syntology.ai/paper/1901.08096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08096"}},"official":{"repos":["sjblim/rnf-ijcnn-2020"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/st-lstm-a-deep-learning-approach-combined","slug":"st-lstm-a-deep-learning-approach-combined","title":"ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term","date":"2019-01-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/artificial-neural-networks","slug":"artificial-neural-networks","title":"Machine learning with neural networks","date":"2019-01-17","arxiv_id":"1901.05639","repositories_listed":1,"syntology":null},{"url":"/paper/sales-demand-forecast-in-e-commerce-using-a","slug":"sales-demand-forecast-in-e-commerce-using-a","title":"Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology","date":"2019-01-13","arxiv_id":"1901.04028","repositories_listed":1,"syntology":null},{"url":"/paper/fastgrnn-a-fast-accurate-stable-and-tiny","slug":"fastgrnn-a-fast-accurate-stable-and-tiny","title":"FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network","date":"2019-01-08","arxiv_id":"1901.02358","repositories_listed":1,"syntology":null},{"url":"/paper/causal-discovery-with-attention-based","slug":"causal-discovery-with-attention-based","title":"Causal Discovery with Attention-Based Convolutional Neural Networks","date":"2019-01-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-cnn-adapted-to-time-series-for-the","slug":"a-cnn-adapted-to-time-series-for-the","title":"A CNN adapted to time series for the classification of Supernovae","date":"2019-01-02","arxiv_id":"1901.00461","repositories_listed":1,"syntology":null},{"url":"/paper/a-full-probabilistic-model-for-yesno-type","slug":"a-full-probabilistic-model-for-yesno-type","title":"A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification","date":"2019-01-02","arxiv_id":"1901.00397","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-deep-learning-framework-for-network","slug":"a-general-deep-learning-framework-for-network","title":"A General Deep Learning Framework for Network Reconstruction and Dynamics Learning","date":"2018-12-30","arxiv_id":"1812.11482","repositories_listed":1,"syntology":null},{"url":"/paper/deep-gated-recurrent-and-convolutional","slug":"deep-gated-recurrent-and-convolutional","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","date":"2018-12-18","arxiv_id":"1812.07683","repositories_listed":1,"syntology":null},{"url":"/paper/dosed-a-deep-learning-approach-to-detect","slug":"dosed-a-deep-learning-approach-to-detect","title":"DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal","date":"2018-12-07","arxiv_id":"1812.04079","repositories_listed":1,"syntology":null},{"url":"/paper/robuststl-a-robust-seasonal-trend","slug":"robuststl-a-robust-seasonal-trend","title":"RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series","date":"2018-12-05","arxiv_id":"1812.01767","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/robuststl-a-robust-seasonal-trend#ran","syntology_url":"https://syntology.ai/paper/1812.01767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01767"}},"official":null}},{"url":"/paper/modeling-irregularly-sampled-clinical-time","slug":"modeling-irregularly-sampled-clinical-time","title":"Modeling Irregularly Sampled Clinical Time Series","date":"2018-12-03","arxiv_id":"1812.00531","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-relationships-by-multi-domain","slug":"extracting-relationships-by-multi-domain","title":"Extracting Relationships by Multi-Domain Matching","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-filter-widths-of-spectral","slug":"learning-filter-widths-of-spectral","title":"Learning filter widths of spectral decompositions with wavelets","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multiple-instance-learning-for-efficient","slug":"multiple-instance-learning-for-efficient","title":"Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/temporal-convolutional-neural-network-for-the","slug":"temporal-convolutional-neural-network-for-the","title":"Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series","date":"2018-11-26","arxiv_id":"1811.10166","repositories_listed":1,"syntology":null},{"url":"/paper/roman-reduced-order-modeling-with-artificial","slug":"roman-reduced-order-modeling-with-artificial","title":"Reduced-order modeling with artificial neurons for gravitational-wave inference","date":"2018-11-13","arxiv_id":"1811.05491","repositories_listed":1,"syntology":null},{"url":"/paper/langevin-gradient-parallel-tempering-for","slug":"langevin-gradient-parallel-tempering-for","title":"Langevin-gradient parallel tempering for Bayesian neural learning","date":"2018-11-11","arxiv_id":"1811.04343","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-cardiac-pathology-classification","slug":"explainable-cardiac-pathology-classification","title":"Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow","date":"2018-11-08","arxiv_id":"1811.03433","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-time-series","slug":"transfer-learning-for-time-series","title":"Transfer learning for time series classification","date":"2018-11-05","arxiv_id":"1811.01533","repositories_listed":1,"syntology":null},{"url":"/paper/challenges-in-detecting-evolutionary-forces","slug":"challenges-in-detecting-evolutionary-forces","title":"Challenges in detecting evolutionary forces in language change using diachronic corpora","date":"2018-11-03","arxiv_id":"1811.01275","repositories_listed":1,"syntology":null},{"url":"/paper/data-driven-perception-of-neuron-point","slug":"data-driven-perception-of-neuron-point","title":"Data-driven Perception of Neuron Point Process with Unknown Unknowns","date":"2018-11-02","arxiv_id":"1811.00688","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-enhanced-stochastic-gradient-mcmc","slug":"clustering-enhanced-stochastic-gradient-mcmc","title":"Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states","date":"2018-10-31","arxiv_id":"1810.13431","repositories_listed":1,"syntology":null},{"url":"/paper/phase-harmonics-and-correlation-invariants-in","slug":"phase-harmonics-and-correlation-invariants-in","title":"Phase Harmonic Correlations and Convolutional Neural Networks","date":"2018-10-29","arxiv_id":"1810.12136","repositories_listed":1,"syntology":null},{"url":"/paper/semi-unsupervised-learning-of-human-activity","slug":"semi-unsupervised-learning-of-human-activity","title":"Semi-unsupervised Learning of Human Activity using Deep Generative Models","date":"2018-10-29","arxiv_id":"1810.12176","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-gradient-mcmc-for-state-space","slug":"stochastic-gradient-mcmc-for-state-space","title":"Stochastic Gradient MCMC for State Space Models","date":"2018-10-22","arxiv_id":"1810.09098","repositories_listed":1,"syntology":null},{"url":"/paper/nonlinear-methods-to-quantify-movement","slug":"nonlinear-methods-to-quantify-movement","title":"Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction Activities","date":"2018-10-17","arxiv_id":"1810.09249","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-temporal-graphs-using-dynamic-time","slug":"comparing-temporal-graphs-using-dynamic-time","title":"Comparing Temporal Graphs Using Dynamic Time Warping","date":"2018-10-15","arxiv_id":"1810.06240","repositories_listed":1,"syntology":null},{"url":"/paper/mining-novel-multivariate-relationships-in","slug":"mining-novel-multivariate-relationships-in","title":"Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks","date":"2018-10-06","arxiv_id":"1810.02950","repositories_listed":1,"syntology":null},{"url":"/paper/deconvolutional-time-series-regression-a","slug":"deconvolutional-time-series-regression-a","title":"Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/infossm-interpretable-unsupervised-learning","slug":"infossm-interpretable-unsupervised-learning","title":"InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics","date":"2018-09-19","arxiv_id":"1809.07109","repositories_listed":1,"syntology":null},{"url":"/paper/on-line-learning-of-linear-dynamical-systems","slug":"on-line-learning-of-linear-dynamical-systems","title":"On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters","date":"2018-09-16","arxiv_id":"1809.05870","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-line-learning-of-linear-dynamical-systems#ran","syntology_url":"https://syntology.ai/paper/1809.05870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05870"}},"official":null}},{"url":"/paper/random-warping-series-a-random-features","slug":"random-warping-series-a-random-features","title":"Random Warping Series: A Random Features Method for Time-Series Embedding","date":"2018-09-14","arxiv_id":"1809.05259","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-time-series-tweaking-via","slug":"explainable-time-series-tweaking-via","title":"Explainable time series tweaking via irreversible and reversible temporal transformations","date":"2018-09-13","arxiv_id":"1809.05183","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-mixtures-of-gaussian-process","slug":"learning-deep-mixtures-of-gaussian-process","title":"Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks","date":"2018-09-12","arxiv_id":"1809.04400","repositories_listed":1,"syntology":null},{"url":"/paper/180902772","slug":"180902772","title":"Order book model with herd behavior exhibiting long-range memory","date":"2018-09-08","arxiv_id":"1809.02772","repositories_listed":1,"syntology":null},{"url":"/paper/constrained-generation-of-semantically-valid","slug":"constrained-generation-of-semantically-valid","title":"Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders","date":"2018-09-07","arxiv_id":"1809.02630","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/constrained-generation-of-semantically-valid#ran","syntology_url":"https://syntology.ai/paper/1809.02630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.02630"}},"official":{"repos":["Microsoft/constrained-graph-variational-autoencoder"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-nonparametric-spectral-estimation","slug":"bayesian-nonparametric-spectral-estimation","title":"Bayesian Nonparametric Spectral Estimation","date":"2018-09-06","arxiv_id":"1809.02196","repositories_listed":1,"syntology":null},{"url":"/paper/casc-context-aware-segmentation-and","slug":"casc-context-aware-segmentation-and","title":"MASA: Motif-Aware State Assignment in Noisy Time Series Data","date":"2018-09-06","arxiv_id":"1809.01819","repositories_listed":1,"syntology":null},{"url":"/paper/elastic-bands-across-the-path-a-new-framework","slug":"elastic-bands-across-the-path-a-new-framework","title":"Elastic bands across the path: A new framework and methods to lower bound DTW","date":"2018-08-29","arxiv_id":"1808.09617","repositories_listed":1,"syntology":null},{"url":"/paper/data-consistency-approach-to-model-validation","slug":"data-consistency-approach-to-model-validation","title":"Data Consistency Approach to Model Validation","date":"2018-08-17","arxiv_id":"1808.05889","repositories_listed":1,"syntology":null},{"url":"/paper/larnn-linear-attention-recurrent-neural","slug":"larnn-linear-attention-recurrent-neural","title":"LARNN: Linear Attention Recurrent Neural Network","date":"2018-08-16","arxiv_id":"1808.05578","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-time-series-classification","slug":"interpretable-time-series-classification","title":"Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains","date":"2018-08-12","arxiv_id":"1808.04022","repositories_listed":1,"syntology":null},{"url":"/paper/a-capsule-network-for-traffic-speed","slug":"a-capsule-network-for-traffic-speed","title":"A Capsule Network for Traffic Speed Prediction in Complex Road Networks","date":"2018-07-23","arxiv_id":"1807.10603","repositories_listed":1,"syntology":null},{"url":"/paper/inferring-multidimensional-rates-of-aging","slug":"inferring-multidimensional-rates-of-aging","title":"Inferring Multidimensional Rates of Aging from Cross-Sectional Data","date":"2018-07-12","arxiv_id":"1807.04709","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inferring-multidimensional-rates-of-aging#ran","syntology_url":"https://syntology.ai/paper/1807.04709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.04709"}},"official":{"repos":["epierson9/multiphenotype_methods"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"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/a-recurrent-neural-network-survival-model","slug":"a-recurrent-neural-network-survival-model","title":"A Recurrent Neural Network Survival Model: Predicting Web User Return Time","date":"2018-07-11","arxiv_id":"1807.04098","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-auto-encoder-model-for-large-scale","slug":"recurrent-auto-encoder-model-for-large-scale","title":"Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis","date":"2018-07-10","arxiv_id":"1807.03710","repositories_listed":1,"syntology":null},{"url":"/paper/a-variational-time-series-feature-extractor","slug":"a-variational-time-series-feature-extractor","title":"A Variational Time Series Feature Extractor for Action Prediction","date":"2018-07-06","arxiv_id":"1807.02350","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-uncertainties-for-deep-learning","slug":"accurate-uncertainties-for-deep-learning","title":"Accurate Uncertainties for Deep Learning Using Calibrated Regression","date":"2018-07-01","arxiv_id":"1807.00263","repositories_listed":1,"syntology":null},{"url":"/paper/stock-movement-prediction-from-tweets-and","slug":"stock-movement-prediction-from-tweets-and","title":"Stock Movement Prediction from Tweets and Historical Prices","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/complex-gated-recurrent-neural-networks","slug":"complex-gated-recurrent-neural-networks","title":"Complex Gated Recurrent Neural Networks","date":"2018-06-21","arxiv_id":"1806.08267","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/complex-gated-recurrent-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1806.08267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08267"}},"official":{"repos":["v0lta/Complex-gated-recurrent-neural-networks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-review-of-network-inference-techniques-for","slug":"a-review-of-network-inference-techniques-for","title":"A Review of Network Inference Techniques for Neural Activation Time Series","date":"2018-06-20","arxiv_id":"1806.08212","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-the-dynamics-of-topical","slug":"quantifying-the-dynamics-of-topical","title":"Quantifying the dynamics of topical fluctuations in language","date":"2018-06-02","arxiv_id":"1806.00699","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-constraint-learning-for","slug":"adversarial-constraint-learning-for","title":"Adversarial Constraint Learning for Structured Prediction","date":"2018-05-27","arxiv_id":"1805.10561","repositories_listed":1,"syntology":null},{"url":"/paper/multivariate-convolutional-sparse-coding-for","slug":"multivariate-convolutional-sparse-coding-for","title":"Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals","date":"2018-05-24","arxiv_id":"1805.09654","repositories_listed":1,"syntology":null},{"url":"/paper/nonlinear-ica-using-auxiliary-variables-and","slug":"nonlinear-ica-using-auxiliary-variables-and","title":"Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning","date":"2018-05-22","arxiv_id":"1805.08651","repositories_listed":1,"syntology":null},{"url":"/paper/newma-a-new-method-for-scalable-model-free","slug":"newma-a-new-method-for-scalable-model-free","title":"NEWMA: a new method for scalable model-free online change-point detection","date":"2018-05-21","arxiv_id":"1805.08061","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/newma-a-new-method-for-scalable-model-free#ran","syntology_url":"https://syntology.ai/paper/1805.08061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.08061"}},"official":{"repos":["lightonai/newma"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dlbi-deep-learning-guided-bayesian-inference","slug":"dlbi-deep-learning-guided-bayesian-inference","title":"DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy","date":"2018-05-20","arxiv_id":"1805.07777","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-regression-analysis-with-deep","slug":"real-time-regression-analysis-with-deep","title":"Real-time regression analysis with deep convolutional neural networks","date":"2018-05-07","arxiv_id":"1805.02716","repositories_listed":1,"syntology":null},{"url":"/paper/eurogames16-evaluating-change-detection-in","slug":"eurogames16-evaluating-change-detection-in","title":"EuroGames16: Evaluating Change Detection in Online Conversation","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-approach-to-fourier","slug":"deep-learning-approach-to-fourier","title":"Deep learning approach to Fourier ptychographic microscopy","date":"2018-04-27","arxiv_id":"1805.00334","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-predicting-asset-returns","slug":"deep-learning-for-predicting-asset-returns","title":"Deep Learning for Predicting Asset Returns","date":"2018-04-25","arxiv_id":"1804.09314","repositories_listed":1,"syntology":null},{"url":"/paper/block-structure-based-time-series-models-for","slug":"block-structure-based-time-series-models-for","title":"Block-Structure Based Time-Series Models For Graph Sequences","date":"2018-04-24","arxiv_id":"1804.08796","repositories_listed":1,"syntology":null},{"url":"/paper/taylors-law-for-human-linguistic-sequences","slug":"taylors-law-for-human-linguistic-sequences","title":"Taylor's law for Human Linguistic Sequences","date":"2018-04-21","arxiv_id":"1804.07893","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-regions-of-maximal-divergence-for","slug":"detecting-regions-of-maximal-divergence-for","title":"Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection","date":"2018-04-19","arxiv_id":"1804.07091","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-networks-for-sequence","slug":"deep-generative-networks-for-sequence","title":"Deep Generative Networks For Sequence Prediction","date":"2018-04-18","arxiv_id":"1804.06546","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-air-pollution-prediction-model","slug":"real-time-air-pollution-prediction-model","title":"Real-time Air Pollution prediction model based on Spatiotemporal Big data","date":"2018-04-05","arxiv_id":"1805.00432","repositories_listed":1,"syntology":null},{"url":"/paper/micompm-a-matlab-octave-toolbox-for","slug":"micompm-a-matlab-octave-toolbox-for","title":"micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observations","date":"2018-03-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-structural-change-in-geographic","slug":"detection-of-structural-change-in-geographic","title":"Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years","date":"2018-03-29","arxiv_id":"1803.11125","repositories_listed":1,"syntology":null},{"url":"/paper/mordred-memory-based-ordinal-regression-deep","slug":"mordred-memory-based-ordinal-regression-deep","title":"MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting","date":"2018-03-26","arxiv_id":"1803.09704","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-simulated-radio-signals","slug":"classification-of-simulated-radio-signals","title":"Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence","date":"2018-03-23","arxiv_id":"1803.08624","repositories_listed":1,"syntology":null},{"url":"/paper/jumping-var-order-statistics-volatility","slug":"jumping-var-order-statistics-volatility","title":"Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk Modeling","date":"2018-03-22","arxiv_id":"1803.07021","repositories_listed":1,"syntology":null},{"url":"/paper/seglearn-a-python-package-for-learning","slug":"seglearn-a-python-package-for-learning","title":"Seglearn: A Python Package for Learning Sequences and Time Series","date":"2018-03-21","arxiv_id":"1803.08118","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-natural-language-processing-with","slug":"dynamic-natural-language-processing-with","title":"Dynamic Natural Language Processing with Recurrence Quantification Analysis","date":"2018-03-19","arxiv_id":"1803.07136","repositories_listed":1,"syntology":null},{"url":"/paper/capturing-structure-implicitly-from-time","slug":"capturing-structure-implicitly-from-time","title":"Capturing Structure Implicitly from Time-Series having Limited Data","date":"2018-03-15","arxiv_id":"1803.05867","repositories_listed":1,"syntology":null},{"url":"/paper/applicability-and-interpretation-of-the","slug":"applicability-and-interpretation-of-the","title":"Applicability and interpretation of the deterministic weighted cepstral distance","date":"2018-03-08","arxiv_id":"1803.03104","repositories_listed":1,"syntology":null},{"url":"/paper/m3fusion-a-deep-learning-architecture-for","slug":"m3fusion-a-deep-learning-architecture-for","title":"M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion","date":"2018-03-05","arxiv_id":"1803.01945","repositories_listed":1,"syntology":null},{"url":"/paper/a-bootstrap-test-to-detect-prominent-granger","slug":"a-bootstrap-test-to-detect-prominent-granger","title":"A bootstrap test to detect prominent Granger-causalities across frequencies","date":"2018-03-01","arxiv_id":"1803.00374","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-time-series-analysis-via","slug":"model-agnostic-time-series-analysis-via","title":"Model Agnostic Time Series Analysis via Matrix Estimation","date":"2018-02-25","arxiv_id":"1802.09064","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multi-view-spatial-temporal-network-for","slug":"deep-multi-view-spatial-temporal-network-for","title":"Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction","date":"2018-02-23","arxiv_id":"1802.08714","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/deep-multi-view-spatial-temporal-network-for#ran","syntology_url":"https://syntology.ai/paper/1802.08714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08714"}},"official":{"repos":["huaxiuyao/DMVST-Net"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/the-affine-wealth-model-an-agent-based-model","slug":"the-affine-wealth-model-an-agent-based-model","title":"The Affine Wealth Model: An agent-based model of asset exchange that allows for negative-wealth agents and its empirical validation","date":"2018-02-15","arxiv_id":"1604.02370","repositories_listed":1,"syntology":null},{"url":"/paper/not-to-cry-wolf-distantly-supervised","slug":"not-to-cry-wolf-distantly-supervised","title":"Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care","date":"2018-02-14","arxiv_id":"1802.05027","repositories_listed":1,"syntology":null},{"url":"/paper/latent-variable-time-varying-network","slug":"latent-variable-time-varying-network","title":"Latent Variable Time-varying Network Inference","date":"2018-02-12","arxiv_id":"1802.03987","repositories_listed":1,"syntology":null},{"url":"/paper/tsviz-demystification-of-deep-learning-models","slug":"tsviz-demystification-of-deep-learning-models","title":"TSViz: Demystification of Deep Learning Models for Time-Series Analysis","date":"2018-02-08","arxiv_id":"1802.02952","repositories_listed":1,"syntology":null},{"url":"/paper/deep-bidirectional-and-unidirectional-lstm","slug":"deep-bidirectional-and-unidirectional-lstm","title":"Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction","date":"2018-01-07","arxiv_id":"1801.02143","repositories_listed":1,"syntology":null},{"url":"/paper/dilated-convolutional-neural-networks-for-3","slug":"dilated-convolutional-neural-networks-for-3","title":"Dilated Convolutional Neural Networks for Time Series Forecasting","date":"2018-01-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dropout-feature-ranking-for-deep-learning","slug":"dropout-feature-ranking-for-deep-learning","title":"Dropout Feature Ranking for Deep Learning Models","date":"2017-12-22","arxiv_id":"1712.08645","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-activity-cycles-with-probabilistic","slug":"estimating-activity-cycles-with-probabilistic","title":"Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend","date":"2017-12-21","arxiv_id":"1712.08235","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-inference-for-adaptive-linear-models","slug":"accurate-inference-for-adaptive-linear-models","title":"Accurate Inference for Adaptive Linear Models","date":"2017-12-18","arxiv_id":"1712.06695","repositories_listed":1,"syntology":null},{"url":"/paper/causal-patterns-extraction-of-multiple-causal","slug":"causal-patterns-extraction-of-multiple-causal","title":"Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis","date":"2017-12-12","arxiv_id":"1712.04221","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-stability-in-predictive-process","slug":"temporal-stability-in-predictive-process","title":"Temporal Stability in Predictive Process Monitoring","date":"2017-12-12","arxiv_id":"1712.04165","repositories_listed":1,"syntology":null},{"url":"/paper/rnn-based-counterfactual-time-series","slug":"rnn-based-counterfactual-time-series","title":"RNN-based counterfactual prediction, with an application to homestead policy and public schooling","date":"2017-12-10","arxiv_id":"1712.03553","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-attention-augmented-bilinear-network","slug":"temporal-attention-augmented-bilinear-network","title":"Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis","date":"2017-12-04","arxiv_id":"1712.00975","repositories_listed":1,"syntology":null},{"url":"/paper/yass-yet-another-spike-sorter","slug":"yass-yet-another-spike-sorter","title":"YASS: Yet Another Spike Sorter","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"d0afd6e593abd4b2677514b79dc1f3b969bcb2ec488ed9de9a6c1ad7920bc987","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}