{"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/gaussian-processes/papers/6","list_of":"/task/gaussian-processes","task":"Gaussian Processes","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":6,"pages_in_order":20,"rows_per_page":100,"rows":[501,600],"of":1963,"counts":{"archive_papers_tagged":1963,"with_a_code_link":685,"where_syntology_ran_a_sample":160,"not_listed_spam_title":0,"listed":1963,"listed_where_code_ran":160,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":131,"every_run_a_failure_of_syntologys_instrument":29,"listed_with_a_run_with_no_instrument_failure":131,"listed_every_run_a_failure_of_syntologys_instrument":29,"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/gaussian-processes","prev":"/task/gaussian-processes/papers/5","next":"/task/gaussian-processes/papers/7","papers":[{"url":"/paper/syn2real-transfer-learning-for-image-1","slug":"syn2real-transfer-learning-for-image-1","title":"Syn2Real Transfer Learning for Image Deraining using Gaussian Processes","date":"2020-06-10","arxiv_id":"2006.05580","repositories_listed":1,"syntology":null},{"url":"/paper/variational-auto-regressive-gaussian","slug":"variational-auto-regressive-gaussian","title":"Variational Auto-Regressive Gaussian Processes for Continual Learning","date":"2020-06-09","arxiv_id":"2006.05468","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":3,"n_instrument":7,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"10 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; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/variational-auto-regressive-gaussian#ran","syntology_url":"https://syntology.ai/paper/2006.05468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05468"}},"official":{"repos":["uber-research/vargp"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/all-your-loss-are-belong-to-bayes","slug":"all-your-loss-are-belong-to-bayes","title":"All your loss are belong to Bayes","date":"2020-06-08","arxiv_id":"2006.04633","repositories_listed":1,"syntology":null},{"url":"/paper/learning-constrained-dynamics-with-gauss-1","slug":"learning-constrained-dynamics-with-gauss-1","title":"Learning Constrained Dynamics with Gauss' Principle adhering Gaussian Processes","date":"2020-06-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-fidelity-high-order-gaussian-processes","slug":"multi-fidelity-high-order-gaussian-processes","title":"Multi-Fidelity High-Order Gaussian Processes for Physical Simulation","date":"2020-06-08","arxiv_id":"2006.04972","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-human-like","slug":"deep-reinforcement-learning-for-human-like","title":"Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars","date":"2020-06-07","arxiv_id":"2006.04218","repositories_listed":1,"syntology":null},{"url":"/paper/a-conditional-one-output-likelihood","slug":"a-conditional-one-output-likelihood","title":"A conditional one-output likelihood formulation for multitask Gaussian processes","date":"2020-06-05","arxiv_id":"2006.03495","repositories_listed":1,"syntology":null},{"url":"/paper/quadruply-stochastic-gaussian-processes","slug":"quadruply-stochastic-gaussian-processes","title":"Quadruply Stochastic Gaussian Processes","date":"2020-06-04","arxiv_id":"2006.03015","repositories_listed":1,"syntology":null},{"url":"/paper/non-euclidean-universal-approximation","slug":"non-euclidean-universal-approximation","title":"Non-Euclidean Universal Approximation","date":"2020-06-03","arxiv_id":"2006.02341","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":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/non-euclidean-universal-approximation#ran","syntology_url":"https://syntology.ai/paper/2006.02341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02341"}},"official":{"repos":["AnastasisKratsios/NeurIPS2020_Non_Euclidean_Universal_Approximation_Example_DNN_Layer_Comparisons"],"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/non-asymptotic-analysis-in-kernel-ridge","slug":"non-asymptotic-analysis-in-kernel-ridge","title":"On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators","date":"2020-06-02","arxiv_id":"2006.01350","repositories_listed":1,"syntology":null},{"url":"/paper/skew-gaussian-processes-for-classification","slug":"skew-gaussian-processes-for-classification","title":"Skew Gaussian Processes for Classification","date":"2020-05-26","arxiv_id":"2005.12987","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-the-mean-field-structured-deep","slug":"beyond-the-mean-field-structured-deep","title":"Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties","date":"2020-05-22","arxiv_id":"2005.11110","repositories_listed":1,"syntology":null},{"url":"/paper/accounting-for-input-noise-in-gaussian","slug":"accounting-for-input-noise-in-gaussian","title":"Accounting for Input Noise in Gaussian Process Parameter Retrieval","date":"2020-05-20","arxiv_id":"2005.09907","repositories_listed":1,"syntology":null},{"url":"/paper/global-inducing-point-variational-posteriors","slug":"global-inducing-point-variational-posteriors","title":"Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes","date":"2020-05-17","arxiv_id":"2005.08140","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":4,"n_ran_checked":4,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":13,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/global-inducing-point-variational-posteriors#ran","syntology_url":"https://syntology.ai/paper/2005.08140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08140"}},"official":{"repos":["LaurenceA/bayesfunc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/when-to-lift-the-lockdown-global-covid-19","slug":"when-to-lift-the-lockdown-global-covid-19","title":"When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes","date":"2020-05-13","arxiv_id":"2005.08837","repositories_listed":1,"syntology":null},{"url":"/paper/planning-from-images-with-deep-latent","slug":"planning-from-images-with-deep-latent","title":"Planning from Images with Deep Latent Gaussian Process Dynamics","date":"2020-05-07","arxiv_id":"2005.03770","repositories_listed":1,"syntology":null},{"url":"/paper/scaled-vecchia-approximation-for-fast","slug":"scaled-vecchia-approximation-for-fast","title":"Scaled Vecchia approximation for fast computer-model emulation","date":"2020-05-01","arxiv_id":"2005.00386","repositories_listed":1,"syntology":null},{"url":"/paper/learning-constrained-dynamics-with-gauss","slug":"learning-constrained-dynamics-with-gauss","title":"Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes","date":"2020-04-23","arxiv_id":"2004.11238","repositories_listed":1,"syntology":null},{"url":"/paper/on-bayesian-search-for-the-feasible-space","slug":"on-bayesian-search-for-the-feasible-space","title":"On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints","date":"2020-04-23","arxiv_id":"2004.11055","repositories_listed":1,"syntology":null},{"url":"/paper/what-do-you-mean-the-role-of-the-mean","slug":"what-do-you-mean-the-role-of-the-mean","title":"What do you Mean? The Role of the Mean Function in Bayesian Optimisation","date":"2020-04-17","arxiv_id":"2004.08349","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/what-do-you-mean-the-role-of-the-mean#ran","syntology_url":"https://syntology.ai/paper/2004.08349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08349"}},"official":{"repos":["georgedeath/bomean"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-robustness-guarantees-for-random","slug":"adversarial-robustness-guarantees-for-random","title":"Adversarial Robustness Guarantees for Random Deep Neural Networks","date":"2020-04-13","arxiv_id":"2004.05923","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarial-robustness-guarantees-for-random#ran","syntology_url":"https://syntology.ai/paper/2004.05923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05923"}},"official":{"repos":["bkiani/Adversarial-robustness-guarantees-for-random-deep-neural-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/direct-loss-minimization-for-sparse-gaussian","slug":"direct-loss-minimization-for-sparse-gaussian","title":"Direct loss minimization algorithms for sparse Gaussian processes","date":"2020-04-07","arxiv_id":"2004.03083","repositories_listed":1,"syntology":null},{"url":"/paper/variational-inference-with-vine-copulas-an","slug":"variational-inference-with-vine-copulas-an","title":"Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration","date":"2020-03-28","arxiv_id":"2003.12890","repositories_listed":1,"syntology":null},{"url":"/paper/knot-selection-in-sparse-gaussian-processes-1","slug":"knot-selection-in-sparse-gaussian-processes-1","title":"Knot Selection in Sparse Gaussian Processes with a Variational Objective Function","date":"2020-03-05","arxiv_id":"2003.02729","repositories_listed":1,"syntology":null},{"url":"/paper/sleipnir-deterministic-and-provably-accurate","slug":"sleipnir-deterministic-and-provably-accurate","title":"SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives","date":"2020-03-05","arxiv_id":"2003.02658","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/sleipnir-deterministic-and-provably-accurate#ran","syntology_url":"https://syntology.ai/paper/2003.02658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02658"}},"official":{"repos":["sdi1100041/SLEIPNIR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-framework-for-interdomain-and-multioutput","slug":"a-framework-for-interdomain-and-multioutput","title":"A Framework for Interdomain and Multioutput Gaussian Processes","date":"2020-03-02","arxiv_id":"2003.01115","repositories_listed":1,"syntology":null},{"url":"/paper/spatiotemporal-learning-of-multivehicle","slug":"spatiotemporal-learning-of-multivehicle","title":"Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios","date":"2020-03-02","arxiv_id":"2003.00759","repositories_listed":1,"syntology":null},{"url":"/paper/stable-behaviour-of-infinitely-wide-deep","slug":"stable-behaviour-of-infinitely-wide-deep","title":"Stable behaviour of infinitely wide deep neural networks","date":"2020-03-01","arxiv_id":"2003.00394","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stable-behaviour-of-infinitely-wide-deep#ran","syntology_url":"https://syntology.ai/paper/2003.00394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00394"}},"official":{"repos":["stepelu/deep-stable"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/automated-augmented-conjugate-inference-for","slug":"automated-augmented-conjugate-inference-for","title":"Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models","date":"2020-02-26","arxiv_id":"2002.11451","repositories_listed":1,"syntology":null},{"url":"/paper/near-linear-time-gaussian-process","slug":"near-linear-time-gaussian-process","title":"Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification","date":"2020-02-23","arxiv_id":"2002.09954","repositories_listed":1,"syntology":null},{"url":"/paper/avoiding-kernel-fixed-points-computing-with","slug":"avoiding-kernel-fixed-points-computing-with","title":"Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks","date":"2020-02-20","arxiv_id":"2002.08517","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-deep-learning-and-a-probabilistic","slug":"bayesian-deep-learning-and-a-probabilistic","title":"Bayesian Deep Learning and a Probabilistic Perspective of Generalization","date":"2020-02-20","arxiv_id":"2002.08791","repositories_listed":1,"syntology":null},{"url":"/paper/kalman-meets-bellman-improving-policy","slug":"kalman-meets-bellman-improving-policy","title":"Kalman meets Bellman: Improving Policy Evaluation through Value Tracking","date":"2020-02-17","arxiv_id":"2002.07171","repositories_listed":1,"syntology":null},{"url":"/paper/mogptk-the-multi-output-gaussian-process","slug":"mogptk-the-multi-output-gaussian-process","title":"MOGPTK: The Multi-Output Gaussian Process Toolkit","date":"2020-02-09","arxiv_id":"2002.03471","repositories_listed":1,"syntology":null},{"url":"/paper/multi-source-deep-gaussian-process-kernel","slug":"multi-source-deep-gaussian-process-kernel","title":"Conditional Deep Gaussian Processes: multi-fidelity kernel learning","date":"2020-02-07","arxiv_id":"2002.02826","repositories_listed":1,"syntology":null},{"url":"/paper/spectrum-dependent-learning-curves-in-kernel","slug":"spectrum-dependent-learning-curves-in-kernel","title":"Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks","date":"2020-02-07","arxiv_id":"2002.02561","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/spectrum-dependent-learning-curves-in-kernel#ran","syntology_url":"https://syntology.ai/paper/2002.02561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02561"}},"official":{"repos":["Pehlevan-Group/NTK_Learning_Curves"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/linearly-constrained-neural-networks","slug":"linearly-constrained-neural-networks","title":"Linearly Constrained Neural Networks","date":"2020-02-05","arxiv_id":"2002.01600","repositories_listed":1,"syntology":null},{"url":"/paper/estimation-of-z-thickness-and-xy-anisotropy","slug":"estimation-of-z-thickness-and-xy-anisotropy","title":"Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian Processes","date":"2020-02-01","arxiv_id":"2002.00228","repositories_listed":1,"syntology":null},{"url":"/paper/multi-class-gaussian-process-classification-1","slug":"multi-class-gaussian-process-classification-1","title":"Multi-class Gaussian Process Classification with Noisy Inputs","date":"2020-01-28","arxiv_id":"2001.10523","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-latent-demand-of-shared-mobility","slug":"estimating-latent-demand-of-shared-mobility","title":"Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes","date":"2020-01-21","arxiv_id":"2001.07402","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-hyperparameter-optimization-with-1","slug":"scalable-hyperparameter-optimization-with-1","title":"Scalable Hyperparameter Optimization with Lazy Gaussian Processes","date":"2020-01-16","arxiv_id":"2001.05726","repositories_listed":1,"syntology":null},{"url":"/paper/considering-discrepancy-when-calibrating-a","slug":"considering-discrepancy-when-calibrating-a","title":"Considering discrepancy when calibrating a mechanistic electrophysiology model","date":"2020-01-13","arxiv_id":"2001.04230","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-representations-using-gaussian","slug":"disentangling-representations-using-gaussian","title":"Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders","date":"2020-01-08","arxiv_id":"2001.02408","repositories_listed":1,"syntology":null},{"url":"/paper/randomly-projected-additive-gaussian","slug":"randomly-projected-additive-gaussian","title":"Randomly Projected Additive Gaussian Processes for Regression","date":"2019-12-30","arxiv_id":"1912.12834","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/randomly-projected-additive-gaussian#ran","syntology_url":"https://syntology.ai/paper/1912.12834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.12834"}},"official":{"repos":["idelbrid/Randomly-Projected-Additive-GPs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/quantile-propagation-for-wasserstein","slug":"quantile-propagation-for-wasserstein","title":"Quantile Propagation for Wasserstein-Approximate Gaussian Processes","date":"2019-12-21","arxiv_id":"1912.10200","repositories_listed":1,"syntology":null},{"url":"/paper/an-interpretable-probabilistic-machine","slug":"an-interpretable-probabilistic-machine","title":"lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data","date":"2019-12-07","arxiv_id":"1912.03549","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-bayesian-preference-learning-for","slug":"scalable-bayesian-preference-learning-for","title":"Scalable Bayesian Preference Learning for Crowds","date":"2019-12-04","arxiv_id":"1912.01987","repositories_listed":1,"syntology":null},{"url":"/paper/wide-feedforward-or-recurrent-neural-networks","slug":"wide-feedforward-or-recurrent-neural-networks","title":"Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-of-weighted-multi-layer-networks-via","slug":"learning-of-weighted-multi-layer-networks-via","title":"Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank Transactions","date":"2019-11-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-fully-natural-gradient-scheme-for-improving","slug":"a-fully-natural-gradient-scheme-for-improving","title":"A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model","date":"2019-11-22","arxiv_id":"1911.10225","repositories_listed":1,"syntology":null},{"url":"/paper/fleet-control-using-coregionalized-gaussian","slug":"fleet-control-using-coregionalized-gaussian","title":"Fleet Control using Coregionalized Gaussian Process Policy Iteration","date":"2019-11-22","arxiv_id":"1911.10121","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-model-aggregation-via-parameter","slug":"statistical-model-aggregation-via-parameter","title":"Statistical Model Aggregation via Parameter Matching","date":"2019-11-01","arxiv_id":"1911.00218","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/statistical-model-aggregation-via-parameter#ran","syntology_url":"https://syntology.ai/paper/1911.00218","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00218"}},"official":{"repos":["IBM/SPAHM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/recovering-bandits","slug":"recovering-bandits","title":"Recovering Bandits","date":"2019-10-31","arxiv_id":"1910.14354","repositories_listed":1,"syntology":null},{"url":"/paper/function-space-distributions-over-kernels","slug":"function-space-distributions-over-kernels","title":"Function-Space Distributions over Kernels","date":"2019-10-29","arxiv_id":"1910.13565","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/function-space-distributions-over-kernels#ran","syntology_url":"https://syntology.ai/paper/1910.13565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13565"}},"official":{"repos":["wjmaddox/spectralgp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-posterior-variational-inference-for","slug":"implicit-posterior-variational-inference-for","title":"Implicit Posterior Variational Inference for Deep Gaussian Processes","date":"2019-10-26","arxiv_id":"1910.11998","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-orthogonal-variational-inference-for","slug":"sparse-orthogonal-variational-inference-for","title":"Sparse Orthogonal Variational Inference for Gaussian Processes","date":"2019-10-23","arxiv_id":"1910.10596","repositories_listed":1,"syntology":null},{"url":"/paper/we-know-where-we-dont-know-3d-bayesian-cnns","slug":"we-know-where-we-dont-know-3d-bayesian-cnns","title":"We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty","date":"2019-10-23","arxiv_id":"1910.10793","repositories_listed":1,"syntology":null},{"url":"/paper/deep-probabilistic-kernels-for-sample","slug":"deep-probabilistic-kernels-for-sample","title":"Deep Kernels with Probabilistic Embeddings for Small-Data Learning","date":"2019-10-13","arxiv_id":"1910.05858","repositories_listed":1,"syntology":null},{"url":"/paper/deep-structured-mixtures-of-gaussian","slug":"deep-structured-mixtures-of-gaussian","title":"Deep Structured Mixtures of Gaussian Processes","date":"2019-10-10","arxiv_id":"1910.04536","repositories_listed":1,"syntology":null},{"url":"/paper/a-learnable-safety-measure","slug":"a-learnable-safety-measure","title":"A Learnable Safety Measure","date":"2019-10-07","arxiv_id":"1910.02835","repositories_listed":1,"syntology":null},{"url":"/paper/partial-separability-and-functional-graphical","slug":"partial-separability-and-functional-graphical","title":"Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes","date":"2019-10-07","arxiv_id":"1910.03134","repositories_listed":1,"syntology":null},{"url":"/paper/no-regret-learning-in-unknown-games-with","slug":"no-regret-learning-in-unknown-games-with","title":"No-Regret Learning in Unknown Games with Correlated Payoffs","date":"2019-09-18","arxiv_id":"1909.08540","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/no-regret-learning-in-unknown-games-with#ran","syntology_url":"https://syntology.ai/paper/1909.08540","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08540"}},"official":null}},{"url":"/paper/compositional-uncertainty-in-deep-gaussian","slug":"compositional-uncertainty-in-deep-gaussian","title":"Compositional uncertainty in deep Gaussian processes","date":"2019-09-17","arxiv_id":"1909.07698","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/compositional-uncertainty-in-deep-gaussian#ran","syntology_url":"https://syntology.ai/paper/1909.07698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07698"}},"official":null}},{"url":"/paper/multi-task-gaussian-processes-and-dilated","slug":"multi-task-gaussian-processes-and-dilated","title":"Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics","date":"2019-08-27","arxiv_id":"1908.10226","repositories_listed":1,"syntology":null},{"url":"/paper/mixture-based-multiple-imputation-models-for","slug":"mixture-based-multiple-imputation-models-for","title":"Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension","date":"2019-08-12","arxiv_id":"1908.04209","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-learning-of-active-subspaces","slug":"sequential-learning-of-active-subspaces","title":"Sequential Learning of Active Subspaces","date":"2019-07-26","arxiv_id":"1907.11572","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/sequential-learning-of-active-subspaces#ran","syntology_url":"https://syntology.ai/paper/1907.11572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.11572"}},"official":null}},{"url":"/paper/structured-variational-inference-in-unstable","slug":"structured-variational-inference-in-unstable","title":"Structured Variational Inference in Unstable Gaussian Process State Space Models","date":"2019-07-16","arxiv_id":"1907.07035","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-parameter-estimation-of-sampled","slug":"efficient-parameter-estimation-of-sampled","title":"The Debiased Spatial Whittle Likelihood","date":"2019-07-04","arxiv_id":"1907.02447","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-thermal-depth-correction-of-rgb-d","slug":"spatio-thermal-depth-correction-of-rgb-d","title":"Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time","date":"2019-07-01","arxiv_id":"1907.00549","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-neural-processes","slug":"sequential-neural-processes","title":"Sequential Neural Processes","date":"2019-06-24","arxiv_id":"1906.10264","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-bayesian-dynamic-covariance-modeling","slug":"scalable-bayesian-dynamic-covariance-modeling","title":"Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes","date":"2019-06-22","arxiv_id":"1906.09360","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/scalable-bayesian-dynamic-covariance-modeling#ran","syntology_url":"https://syntology.ai/paper/1906.09360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.09360"}},"official":null}},{"url":"/paper/multi-resolution-multi-task-gaussian","slug":"multi-resolution-multi-task-gaussian","title":"Multi-resolution Multi-task Gaussian Processes","date":"2019-06-19","arxiv_id":"1906.08344","repositories_listed":1,"syntology":null},{"url":"/paper/variational-gaussian-processes-with-signature","slug":"variational-gaussian-processes-with-signature","title":"Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances","date":"2019-06-19","arxiv_id":"1906.08215","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/variational-gaussian-processes-with-signature#ran","syntology_url":"https://syntology.ai/paper/1906.08215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.08215"}},"official":{"repos":["tgcsaba/GPSig"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/kernelized-capsule-networks","slug":"kernelized-capsule-networks","title":"Kernelized Capsule Networks","date":"2019-06-07","arxiv_id":"1906.03164","repositories_listed":1,"syntology":null},{"url":"/paper/approximate-inference-turns-deep-networks","slug":"approximate-inference-turns-deep-networks","title":"Approximate Inference Turns Deep Networks into Gaussian Processes","date":"2019-06-05","arxiv_id":"1906.01930","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/approximate-inference-turns-deep-networks#ran","syntology_url":"https://syntology.ai/paper/1906.01930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01930"}},"official":{"repos":["team-approx-bayes/dnn2gp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/streaming-variational-monte-carlo","slug":"streaming-variational-monte-carlo","title":"Streaming Variational Monte Carlo","date":"2019-06-04","arxiv_id":"1906.01549","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/streaming-variational-monte-carlo#ran","syntology_url":"https://syntology.ai/paper/1906.01549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01549"}},"official":{"repos":["catniplab/svmc"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monotonic-gaussian-process-flow","slug":"monotonic-gaussian-process-flow","title":"Monotonic Gaussian Process Flow","date":"2019-05-30","arxiv_id":"1905.12930","repositories_listed":1,"syntology":null},{"url":"/paper/recursive-estimation-for-sparse-gaussian","slug":"recursive-estimation-for-sparse-gaussian","title":"Recursive Estimation for Sparse Gaussian Process Regression","date":"2019-05-28","arxiv_id":"1905.11711","repositories_listed":1,"syntology":null},{"url":"/paper/robustness-quantification-for-classification","slug":"robustness-quantification-for-classification","title":"Adversarial Robustness Guarantees for Classification with Gaussian Processes","date":"2019-05-28","arxiv_id":"1905.11876","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-gaussian-processes-for-online","slug":"sequential-gaussian-processes-for-online","title":"Sequential Gaussian Processes for Online Learning of Nonstationary Functions","date":"2019-05-24","arxiv_id":"1905.10003","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-deep-gaussian-process-models-for","slug":"efficient-deep-gaussian-process-models-for","title":"Efficient Deep Gaussian Process Models for Variable-Sized Input","date":"2019-05-16","arxiv_id":"1905.06982","repositories_listed":1,"syntology":null},{"url":"/paper/deep-gaussian-processes-with-importance","slug":"deep-gaussian-processes-with-importance","title":"Deep Gaussian Processes with Importance-Weighted Variational Inference","date":"2019-05-14","arxiv_id":"1905.05435","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-gaussian-processes-with-importance#ran","syntology_url":"https://syntology.ai/paper/1905.05435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.05435"}},"official":{"repos":["hughsalimbeni/DGPs_with_IWVI"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/190503406","slug":"190503406","title":"Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models","date":"2019-05-09","arxiv_id":"1905.03406","repositories_listed":1,"syntology":null},{"url":"/paper/know-your-boundaries-constraining-gaussian","slug":"know-your-boundaries-constraining-gaussian","title":"Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features","date":"2019-04-10","arxiv_id":"1904.05207","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-variational-inference","slug":"generalized-variational-inference","title":"Generalized Variational Inference: Three arguments for deriving new Posteriors","date":"2019-04-03","arxiv_id":"1904.02063","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-safe-reinforcement-learning","slug":"end-to-end-safe-reinforcement-learning","title":"End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks","date":"2019-03-21","arxiv_id":"1903.08792","repositories_listed":1,"syntology":null},{"url":"/paper/deep-gaussian-processes-for-multi-fidelity","slug":"deep-gaussian-processes-for-multi-fidelity","title":"Deep Gaussian Processes for Multi-fidelity Modeling","date":"2019-03-18","arxiv_id":"1903.07320","repositories_listed":1,"syntology":null},{"url":"/paper/gaussian-process-optimization-with-adaptive","slug":"gaussian-process-optimization-with-adaptive","title":"Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret","date":"2019-03-13","arxiv_id":"1903.05594","repositories_listed":1,"syntology":null},{"url":"/paper/stay-ahead-of-poachers-illegal-wildlife","slug":"stay-ahead-of-poachers-illegal-wildlife","title":"Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations","date":"2019-03-08","arxiv_id":"1903.06669","repositories_listed":1,"syntology":null},{"url":"/paper/deeper-connections-between-neural-networks","slug":"deeper-connections-between-neural-networks","title":"Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning","date":"2019-02-27","arxiv_id":"1902.10350","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/deeper-connections-between-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1902.10350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10350"}},"official":null}},{"url":"/paper/unsupervised-visual-domain-adaptation-a-deep","slug":"unsupervised-visual-domain-adaptation-a-deep","title":"Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach","date":"2019-02-23","arxiv_id":"1902.08727","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/unsupervised-visual-domain-adaptation-a-deep#ran","syntology_url":"https://syntology.ai/paper/1902.08727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.08727"}},"official":null}},{"url":"/paper/ares-and-mars-adversarial-and-mmd-minimizing","slug":"ares-and-mars-adversarial-and-mmd-minimizing","title":"AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs","date":"2019-02-22","arxiv_id":"1902.08480","repositories_listed":1,"syntology":null},{"url":"/paper/wide-neural-networks-of-any-depth-evolve-as","slug":"wide-neural-networks-of-any-depth-evolve-as","title":"Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent","date":"2019-02-18","arxiv_id":"1902.06720","repositories_listed":1,"syntology":null},{"url":"/paper/functional-regularisation-for-continual","slug":"functional-regularisation-for-continual","title":"Functional Regularisation for Continual Learning with Gaussian Processes","date":"2019-01-31","arxiv_id":"1901.11356","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/functional-regularisation-for-continual#ran","syntology_url":"https://syntology.ai/paper/1901.11356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.11356"}},"official":null}},{"url":"/paper/probo-a-framework-for-using-probabilistic","slug":"probo-a-framework-for-using-probabilistic","title":"ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language","date":"2019-01-31","arxiv_id":"1901.11515","repositories_listed":1,"syntology":null},{"url":"/paper/active-learning-with-gaussian-processes-for","slug":"active-learning-with-gaussian-processes-for","title":"Active Learning with Gaussian Processes for High Throughput Phenotyping","date":"2019-01-21","arxiv_id":"1901.06803","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-cnn-for-lung-nodule","slug":"multi-level-cnn-for-lung-nodule","title":"Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization","date":"2019-01-02","arxiv_id":"1901.00276","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-squared-exponential-covariance","slug":"evaluating-the-squared-exponential-covariance","title":"Evaluating the squared-exponential covariance function in Gaussian processes with integral observations","date":"2018-12-18","arxiv_id":"1812.07319","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-layers-a-module-for-neural-network","slug":"bayesian-layers-a-module-for-neural-network","title":"Bayesian Layers: A Module for Neural Network Uncertainty","date":"2018-12-10","arxiv_id":"1812.03973","repositories_listed":1,"syntology":null},{"url":"/paper/neural-non-stationary-spectral-kernel","slug":"neural-non-stationary-spectral-kernel","title":"Neural Non-Stationary Spectral Kernel","date":"2018-11-27","arxiv_id":"1811.10978","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/neural-non-stationary-spectral-kernel#ran","syntology_url":"https://syntology.ai/paper/1811.10978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10978"}},"official":{"repos":["sremes/nssm-gp"],"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"]}}}],"record_sha256":"893c7f300e02f6747fc7d5f9d4b05fbda1b922ea37476078804a1e8dd6c404d3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}