{"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/variational-inference/papers/4","list_of":"/task/variational-inference","task":"Variational Inference","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":4,"pages_in_order":23,"rows_per_page":100,"rows":[301,400],"of":2274,"counts":{"archive_papers_tagged":2274,"with_a_code_link":880,"where_syntology_ran_a_sample":269,"not_listed_spam_title":0,"listed":2274,"listed_where_code_ran":269,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":223,"every_run_a_failure_of_syntologys_instrument":46,"listed_with_a_run_with_no_instrument_failure":223,"listed_every_run_a_failure_of_syntologys_instrument":46,"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/variational-inference","prev":"/task/variational-inference/papers/3","next":"/task/variational-inference/papers/5","papers":[{"url":"/paper/discovering-mixtures-of-structural-causal","slug":"discovering-mixtures-of-structural-causal","title":"Discovering Mixtures of Structural Causal Models from Time Series Data","date":"2023-10-10","arxiv_id":"2310.06312","repositories_listed":1,"syntology":null},{"url":"/paper/high-dimensional-causal-inference-with","slug":"high-dimensional-causal-inference-with","title":"High Dimensional Causal Inference with Variational Backdoor Adjustment","date":"2023-10-09","arxiv_id":"2310.06100","repositories_listed":1,"syntology":null},{"url":"/paper/subspace-identification-for-multi-source","slug":"subspace-identification-for-multi-source","title":"Subspace Identification for Multi-Source Domain Adaptation","date":"2023-10-07","arxiv_id":"2310.04723","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/subspace-identification-for-multi-source#ran","syntology_url":"https://syntology.ai/paper/2310.04723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04723"}},"official":{"repos":["jozerozero/subspace_identification"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-variational-bayesian-phylogenetic","slug":"improved-variational-bayesian-phylogenetic","title":"Improved Variational Bayesian Phylogenetic Inference using Mixtures","date":"2023-10-02","arxiv_id":"2310.00941","repositories_listed":1,"syntology":null},{"url":"/paper/logicmp-a-neuro-symbolic-approach-for","slug":"logicmp-a-neuro-symbolic-approach-for","title":"LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints","date":"2023-09-27","arxiv_id":"2309.15458","repositories_listed":1,"syntology":null},{"url":"/paper/bayesdll-bayesian-deep-learning-library","slug":"bayesdll-bayesian-deep-learning-library","title":"BayesDLL: Bayesian Deep Learning Library","date":"2023-09-22","arxiv_id":"2309.12928","repositories_listed":1,"syntology":null},{"url":"/paper/neural-operator-variational-inference-based","slug":"neural-operator-variational-inference-based","title":"Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes","date":"2023-09-22","arxiv_id":"2309.12658","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-sparsification-for-deep-neural","slug":"bayesian-sparsification-for-deep-neural","title":"Bayesian sparsification for deep neural networks with Bayesian model reduction","date":"2023-09-21","arxiv_id":"2309.12095","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":0,"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/bayesian-sparsification-for-deep-neural#ran","syntology_url":"https://syntology.ai/paper/2309.12095","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12095"}},"official":{"repos":["dimarkov/bmr4pml"],"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/towards-the-topmost-a-topic-modeling-system","slug":"towards-the-topmost-a-topic-modeling-system","title":"Towards the TopMost: A Topic Modeling System Toolkit","date":"2023-09-13","arxiv_id":"2309.06908","repositories_listed":1,"syntology":null},{"url":"/paper/amortised-inference-in-bayesian-neural","slug":"amortised-inference-in-bayesian-neural","title":"Amortised Inference in Bayesian Neural Networks","date":"2023-09-06","arxiv_id":"2309.03018","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-variational-inference-for-online","slug":"distributed-variational-inference-for-online","title":"Distributed Variational Inference for Online Supervised Learning","date":"2023-09-05","arxiv_id":"2309.02606","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-bayesian-computational-imaging-with","slug":"efficient-bayesian-computational-imaging-with","title":"Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior","date":"2023-09-05","arxiv_id":"2309.01949","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":13,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/efficient-bayesian-computational-imaging-with#ran","syntology_url":"https://syntology.ai/paper/2309.01949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01949"}},"official":{"repos":["berthyf96/score_prior"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cognition-mode-aware-variational","slug":"cognition-mode-aware-variational","title":"Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing","date":"2023-09-03","arxiv_id":"2309.01179","repositories_listed":1,"syntology":null},{"url":"/paper/finding-the-perfect-fit-applying-regression","slug":"finding-the-perfect-fit-applying-regression","title":"Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0","date":"2023-08-23","arxiv_id":"2308.11854","repositories_listed":1,"syntology":null},{"url":"/paper/semi-implicit-variational-inference-via-score","slug":"semi-implicit-variational-inference-via-score","title":"Semi-Implicit Variational Inference via Score Matching","date":"2023-08-19","arxiv_id":"2308.10014","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":6,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-implicit-variational-inference-via-score#ran","syntology_url":"https://syntology.ai/paper/2308.10014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10014"}},"official":{"repos":["longinyu/sivism"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/score-priors-guided-deep-variational","slug":"score-priors-guided-deep-variational","title":"Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising","date":"2023-08-09","arxiv_id":"2308.04682","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-based-inference-using-surjective","slug":"simulation-based-inference-using-surjective","title":"Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation","date":"2023-08-02","arxiv_id":"2308.01054","repositories_listed":1,"syntology":null},{"url":"/paper/bayesdag-gradient-based-posterior-sampling","slug":"bayesdag-gradient-based-posterior-sampling","title":"BayesDAG: Gradient-Based Posterior Inference for Causal Discovery","date":"2023-07-26","arxiv_id":"2307.13917","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":1,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bayesdag-gradient-based-posterior-sampling#ran","syntology_url":"https://syntology.ai/paper/2307.13917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13917"}},"official":{"repos":["microsoft/Project-BayesDAG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/learning-minimal-representations-of","slug":"learning-minimal-representations-of","title":"Learning minimal representations of stochastic processes with variational autoencoders","date":"2023-07-21","arxiv_id":"2307.11608","repositories_listed":1,"syntology":null},{"url":"/paper/vits-variational-inference-thomson-sampling","slug":"vits-variational-inference-thomson-sampling","title":"VITS : Variational Inference Thompson Sampling for contextual bandits","date":"2023-07-19","arxiv_id":"2307.10167","repositories_listed":1,"syntology":null},{"url":"/paper/safe-reinforcement-learning-as-wasserstein","slug":"safe-reinforcement-learning-as-wasserstein","title":"Probabilistic Constrained Reinforcement Learning with Formal Interpretability","date":"2023-07-13","arxiv_id":"2307.07084","repositories_listed":1,"syntology":null},{"url":"/paper/causal-neural-graph-collaborative-filtering","slug":"causal-neural-graph-collaborative-filtering","title":"Neural Causal Graph Collaborative Filtering","date":"2023-07-10","arxiv_id":"2307.04384","repositories_listed":1,"syntology":null},{"url":"/paper/linfa-a-python-library-for-variational","slug":"linfa-a-python-library-for-variational","title":"LINFA: a Python library for variational inference with normalizing flow and annealing","date":"2023-07-10","arxiv_id":"2307.04675","repositories_listed":1,"syntology":null},{"url":"/paper/learning-space-time-continuous-neural-pdes","slug":"learning-space-time-continuous-neural-pdes","title":"Learning Space-Time Continuous Neural PDEs from Partially Observed States","date":"2023-07-09","arxiv_id":"2307.04110","repositories_listed":1,"syntology":null},{"url":"/paper/geophy-differentiable-phylogenetic-inference-1","slug":"geophy-differentiable-phylogenetic-inference-1","title":"GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies","date":"2023-07-07","arxiv_id":"2307.03675","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/geophy-differentiable-phylogenetic-inference-1#ran","syntology_url":"https://syntology.ai/paper/2307.03675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.03675"}},"official":{"repos":["m1m0r1/geophy"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-probabilistic-data-driven-closure-model-for","slug":"a-probabilistic-data-driven-closure-model-for","title":"A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty","date":"2023-07-05","arxiv_id":"2307.02432","repositories_listed":1,"syntology":null},{"url":"/paper/transport-variational-inference-and","slug":"transport-variational-inference-and","title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","date":"2023-07-03","arxiv_id":"2307.01050","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/transport-variational-inference-and#ran","syntology_url":"https://syntology.ai/paper/2307.01050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01050"}},"official":{"repos":["shreyaspadhy/cmcd"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/latent-sdes-on-homogeneous-spaces-1","slug":"latent-sdes-on-homogeneous-spaces-1","title":"Latent SDEs on Homogeneous Spaces","date":"2023-06-28","arxiv_id":"2306.16248","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/latent-sdes-on-homogeneous-spaces-1#ran","syntology_url":"https://syntology.ai/paper/2306.16248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16248"}},"official":{"repos":["plus-rkwitt/latentsdeonhs"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-language-networks-joint-prompt-training","slug":"deep-language-networks-joint-prompt-training","title":"Joint Prompt Optimization of Stacked LLMs using Variational Inference","date":"2023-06-21","arxiv_id":"2306.12509","repositories_listed":1,"syntology":{"n":19,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":16,"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) · 16 unverified","sample_list":"/paper/deep-language-networks-joint-prompt-training#ran","syntology_url":"https://syntology.ai/paper/2306.12509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12509"}},"official":{"repos":["microsoft/deep-language-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":16,"ran_from_kinds":["official"]}}},{"url":"/paper/a-hierarchical-bayesian-model-for-deep-few","slug":"a-hierarchical-bayesian-model-for-deep-few","title":"A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning","date":"2023-06-16","arxiv_id":"2306.09702","repositories_listed":1,"syntology":null},{"url":"/paper/structured-cooperative-learning-with","slug":"structured-cooperative-learning-with","title":"Structured Cooperative Learning with Graphical Model Priors","date":"2023-06-16","arxiv_id":"2306.09595","repositories_listed":1,"syntology":{"n":27,"n_ran":17,"n_constructed":0,"n_ran_checked":10,"n_instrument":7,"n_unverified":10,"n_honours":6,"n_violates":0,"n_no_contract":4,"n_pointer_only":27,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 6 honoured, 0 violated, 4 with no contract checked; 7 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/structured-cooperative-learning-with#ran","syntology_url":"https://syntology.ai/paper/2306.09595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09595"}},"official":{"repos":["shuangtongli/scool"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-positive-incentive-noise-how","slug":"variational-positive-incentive-noise-how","title":"Variational Positive-incentive Noise: How Noise Benefits Models","date":"2023-06-13","arxiv_id":"2306.07651","repositories_listed":1,"syntology":null},{"url":"/paper/vifs-an-end-to-end-variational-inference-for","slug":"vifs-an-end-to-end-variational-inference-for","title":"VIFS: An End-to-End Variational Inference for Foley Sound Synthesis","date":"2023-06-08","arxiv_id":"2306.05004","repositories_listed":1,"syntology":null},{"url":"/paper/improving-hyperparameter-learning-under","slug":"improving-hyperparameter-learning-under","title":"Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models","date":"2023-06-07","arxiv_id":"2306.04201","repositories_listed":1,"syntology":{"n":19,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-hyperparameter-learning-under#ran","syntology_url":"https://syntology.ai/paper/2306.04201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04201"}},"official":{"repos":["aaltoml/improved-hyperparameter-learning"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/change-diffusion-change-detection-map","slug":"change-diffusion-change-detection-map","title":"GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM","date":"2023-06-06","arxiv_id":"2306.03424","repositories_listed":1,"syntology":null},{"url":"/paper/input-gradient-diversity-for-neural-network","slug":"input-gradient-diversity-for-neural-network","title":"Input-gradient space particle inference for neural network ensembles","date":"2023-06-05","arxiv_id":"2306.02775","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/input-gradient-diversity-for-neural-network#ran","syntology_url":"https://syntology.ai/paper/2306.02775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02775"}},"official":{"repos":["aaltopml/forde"],"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/path-specific-counterfactual-fairness-for","slug":"path-specific-counterfactual-fairness-for","title":"Path-Specific Counterfactual Fairness for Recommender Systems","date":"2023-06-05","arxiv_id":"2306.02615","repositories_listed":1,"syntology":null},{"url":"/paper/contrabar-contrastive-bayes-adaptive-deep-rl","slug":"contrabar-contrastive-bayes-adaptive-deep-rl","title":"ContraBAR: Contrastive Bayes-Adaptive Deep RL","date":"2023-06-04","arxiv_id":"2306.02418","repositories_listed":1,"syntology":null},{"url":"/paper/an-information-field-theory-approach-to","slug":"an-information-field-theory-approach-to","title":"An information field theory approach to Bayesian state and parameter estimation in dynamical systems","date":"2023-06-03","arxiv_id":"2306.02150","repositories_listed":1,"syntology":null},{"url":"/paper/variational-gaussian-process-diffusion","slug":"variational-gaussian-process-diffusion","title":"Variational Gaussian Process Diffusion Processes","date":"2023-06-03","arxiv_id":"2306.02066","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/variational-gaussian-process-diffusion#ran","syntology_url":"https://syntology.ai/paper/2306.02066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02066"}},"official":{"repos":["aaltoml/vi-diffusion-processes"],"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/learning-to-solve-bayesian-inverse-problems","slug":"learning-to-solve-bayesian-inverse-problems","title":"Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides","date":"2023-05-31","arxiv_id":"2305.20004","repositories_listed":1,"syntology":null},{"url":"/paper/low-rank-extended-kalman-filtering-for-online","slug":"low-rank-extended-kalman-filtering-for-online","title":"Low-rank extended Kalman filtering for online learning of neural networks from streaming data","date":"2023-05-31","arxiv_id":"2305.19535","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/low-rank-extended-kalman-filtering-for-online#ran","syntology_url":"https://syntology.ai/paper/2305.19535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19535"}},"official":{"repos":["probml/rebayes"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-markov-jump-processes","slug":"neural-markov-jump-processes","title":"Neural Markov Jump Processes","date":"2023-05-31","arxiv_id":"2305.19744","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":0,"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/neural-markov-jump-processes#ran","syntology_url":"https://syntology.ai/paper/2305.19744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19744"}},"official":{"repos":["pseifner/neuralmjp"],"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/learning-a-structural-causal-model-for","slug":"learning-a-structural-causal-model-for","title":"Learning a Structural Causal Model for Intuition Reasoning in Conversation","date":"2023-05-28","arxiv_id":"2305.17727","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-random-partition-models-1","slug":"differentiable-random-partition-models-1","title":"Differentiable Random Partition Models","date":"2023-05-26","arxiv_id":"2305.16841","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-calibration-check-bayesian","slug":"discriminative-calibration-check-bayesian","title":"Discriminative calibration: Check Bayesian computation from simulations and flexible classifier","date":"2023-05-24","arxiv_id":"2305.14593","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/discriminative-calibration-check-bayesian#ran","syntology_url":"https://syntology.ai/paper/2305.14593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14593"}},"official":{"repos":["yao-yl/disccalibration"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/df2m-an-explainable-deep-bayesian","slug":"df2m-an-explainable-deep-bayesian","title":"Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization","date":"2023-05-23","arxiv_id":"2305.14543","repositories_listed":1,"syntology":null},{"url":"/paper/diva-a-dirichlet-process-based-incremental","slug":"diva-a-dirichlet-process-based-incremental","title":"DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder","date":"2023-05-23","arxiv_id":"2305.14067","repositories_listed":1,"syntology":null},{"url":"/paper/variational-inference-with-coverage","slug":"variational-inference-with-coverage","title":"Variational Inference with Coverage Guarantees in Simulation-Based Inference","date":"2023-05-23","arxiv_id":"2305.14275","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-aware-personalized-federated-1","slug":"confidence-aware-personalized-federated-1","title":"Confidence-aware Personalized Federated Learning via Variational Expectation Maximization","date":"2023-05-21","arxiv_id":"2305.12557","repositories_listed":1,"syntology":null},{"url":"/paper/seismic-random-noise-attenuation-based-on-non","slug":"seismic-random-noise-attenuation-based-on-non","title":"Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling","date":"2023-05-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fully-bayesian-vib-deepssm","slug":"fully-bayesian-vib-deepssm","title":"Fully Bayesian VIB-DeepSSM","date":"2023-05-09","arxiv_id":"2305.05797","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fully-bayesian-vib-deepssm#ran","syntology_url":"https://syntology.ai/paper/2305.05797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05797"}},"official":{"repos":["jadie1/bvib-deepssm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-inference-for-bayesian-neural","slug":"variational-inference-for-bayesian-neural","title":"Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty","date":"2023-05-01","arxiv_id":"2305.00934","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/variational-inference-for-bayesian-neural#ran","syntology_url":"https://syntology.ai/paper/2305.00934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00934"}},"official":{"repos":["aliaksah/variational-inference-for-bayesian-neural-networks-under-model-and-parameter-uncertainty"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/score-based-diffusion-models-as-principled","slug":"score-based-diffusion-models-as-principled","title":"Score-Based Diffusion Models as Principled Priors for Inverse Imaging","date":"2023-04-23","arxiv_id":"2304.11751","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-inference-on-brain-computer","slug":"bayesian-inference-on-brain-computer","title":"Bayesian Inference on Brain-Computer Interfaces via GLASS","date":"2023-04-14","arxiv_id":"2304.07401","repositories_listed":1,"syntology":null},{"url":"/paper/black-box-variational-inference-with-a","slug":"black-box-variational-inference-with-a","title":"Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box","date":"2023-04-11","arxiv_id":"2304.05527","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/black-box-variational-inference-with-a#ran","syntology_url":"https://syntology.ai/paper/2304.05527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05527"}},"official":{"repos":["rgiordan/dadvipaper"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-neural-networks-via-mcmc-a-python","slug":"bayesian-neural-networks-via-mcmc-a-python","title":"Bayesian neural networks via MCMC: a Python-based tutorial","date":"2023-04-02","arxiv_id":"2304.02595","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-alternating-minimization-solvers","slug":"efficient-alternating-minimization-solvers","title":"Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning","date":"2023-03-28","arxiv_id":"2303.15866","repositories_listed":1,"syntology":null},{"url":"/paper/variational-inference-for-longitudinal-data","slug":"variational-inference-for-longitudinal-data","title":"Variational Inference for Longitudinal Data Using Normalizing Flows","date":"2023-03-24","arxiv_id":"2303.14220","repositories_listed":1,"syntology":null},{"url":"/paper/dynamical-hyperspectral-unmixing-with","slug":"dynamical-hyperspectral-unmixing-with","title":"Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks","date":"2023-03-19","arxiv_id":"2303.10566","repositories_listed":1,"syntology":null},{"url":"/paper/fast-post-process-bayesian-inference-with","slug":"fast-post-process-bayesian-inference-with","title":"Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature","date":"2023-03-09","arxiv_id":"2303.05263","repositories_listed":1,"syntology":null},{"url":"/paper/variational-inference-for-neyman-scott","slug":"variational-inference-for-neyman-scott","title":"Variational Inference for Neyman-Scott Processes","date":"2023-03-07","arxiv_id":"2303.03701","repositories_listed":1,"syntology":null},{"url":"/paper/mfai-a-scalable-bayesian-matrix-factorization","slug":"mfai-a-scalable-bayesian-matrix-factorization","title":"MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information","date":"2023-03-05","arxiv_id":"2303.02566","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-segmentation-as-gaussian-process","slug":"interactive-segmentation-as-gaussian-process","title":"Interactive Segmentation as Gaussian Process Classification","date":"2023-02-28","arxiv_id":"2302.14578","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/interactive-segmentation-as-gaussian-process#ran","syntology_url":"https://syntology.ai/paper/2302.14578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14578"}},"official":{"repos":["zmhhmz/gpcis_cvpr2023"],"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/natural-gradient-hybrid-variational-inference","slug":"natural-gradient-hybrid-variational-inference","title":"Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models","date":"2023-02-27","arxiv_id":"2302.13536","repositories_listed":1,"syntology":null},{"url":"/paper/a-targeted-accuracy-diagnostic-for","slug":"a-targeted-accuracy-diagnostic-for","title":"A Targeted Accuracy Diagnostic for Variational Approximations","date":"2023-02-24","arxiv_id":"2302.12419","repositories_listed":1,"syntology":null},{"url":"/paper/energy-based-test-sample-adaptation-for","slug":"energy-based-test-sample-adaptation-for","title":"Energy-Based Test Sample Adaptation for Domain Generalization","date":"2023-02-22","arxiv_id":"2302.11215","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/energy-based-test-sample-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2302.11215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.11215"}},"official":{"repos":["zzzx1224/ebtsa-iclr2023"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-flows-for-sampling-mean-field-models","slug":"gradient-flows-for-sampling-mean-field-models","title":"Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance","date":"2023-02-21","arxiv_id":"2302.11024","repositories_listed":1,"syntology":null},{"url":"/paper/free-form-variational-inference-for-gaussian","slug":"free-form-variational-inference-for-gaussian","title":"Free-Form Variational Inference for Gaussian Process State-Space Models","date":"2023-02-20","arxiv_id":"2302.09921","repositories_listed":1,"syntology":null},{"url":"/paper/the-shrinkage-delinkage-trade-off-an-analysis","slug":"the-shrinkage-delinkage-trade-off-an-analysis","title":"The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference","date":"2023-02-17","arxiv_id":"2302.09163","repositories_listed":1,"syntology":null},{"url":"/paper/gflownet-em-for-learning-compositional-latent","slug":"gflownet-em-for-learning-compositional-latent","title":"GFlowNet-EM for learning compositional latent variable models","date":"2023-02-13","arxiv_id":"2302.06576","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/gflownet-em-for-learning-compositional-latent#ran","syntology_url":"https://syntology.ai/paper/2302.06576","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06576"}},"official":{"repos":["gfnorg/gflownet-em"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-bayesian-neural-networks-via","slug":"variational-bayesian-neural-networks-via","title":"Variational Bayesian Neural Networks via Resolution of Singularities","date":"2023-02-13","arxiv_id":"2302.06035","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-normalizing-flows-for-1","slug":"differentially-private-normalizing-flows-for-1","title":"Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records","date":"2023-02-11","arxiv_id":"2302.05787","repositories_listed":1,"syntology":null},{"url":"/paper/a-benchmark-on-uncertainty-quantification-for","slug":"a-benchmark-on-uncertainty-quantification-for","title":"A Benchmark on Uncertainty Quantification for Deep Learning Prognostics","date":"2023-02-09","arxiv_id":"2302.04730","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-as-variational-inference-a","slug":"federated-learning-as-variational-inference-a","title":"Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach","date":"2023-02-08","arxiv_id":"2302.04228","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/federated-learning-as-variational-inference-a#ran","syntology_url":"https://syntology.ai/paper/2302.04228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04228"}},"official":{"repos":["hanguo97/expectation-propagation"],"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/prior-density-learning-in-variational","slug":"prior-density-learning-in-variational","title":"Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference","date":"2023-02-06","arxiv_id":"2302.02522","repositories_listed":1,"syntology":null},{"url":"/paper/direct-uncertainty-quantification","slug":"direct-uncertainty-quantification","title":"Variational Inference on the Final-Layer Output of Neural Networks","date":"2023-02-05","arxiv_id":"2302.02420","repositories_listed":1,"syntology":null},{"url":"/paper/a-theory-of-continuous-generative-flow","slug":"a-theory-of-continuous-generative-flow","title":"A theory of continuous generative flow networks","date":"2023-01-30","arxiv_id":"2301.12594","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/a-theory-of-continuous-generative-flow#ran","syntology_url":"https://syntology.ai/paper/2301.12594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12594"}},"official":{"repos":["saleml/continuous-gfn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-latent-branching-model-for-off","slug":"variational-latent-branching-model-for-off","title":"Variational Latent Branching Model for Off-Policy Evaluation","date":"2023-01-28","arxiv_id":"2301.12056","repositories_listed":1,"syntology":null},{"url":"/paper/coin-sampling-gradient-based-bayesian","slug":"coin-sampling-gradient-based-bayesian","title":"Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates","date":"2023-01-26","arxiv_id":"2301.11294","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/coin-sampling-gradient-based-bayesian#ran","syntology_url":"https://syntology.ai/paper/2301.11294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11294"}},"official":{"repos":["louissharrock/coin-svgd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-dynamic-focused-topic-model-1","slug":"neural-dynamic-focused-topic-model-1","title":"Neural Dynamic Focused Topic Model","date":"2023-01-26","arxiv_id":"2301.10988","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":0,"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/neural-dynamic-focused-topic-model-1#ran","syntology_url":"https://syntology.ai/paper/2301.10988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10988"}},"official":{"repos":["cvejoski/Neural-Dynamic-Focused-Topic-Model"],"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/normflows-a-pytorch-package-for-normalizing","slug":"normflows-a-pytorch-package-for-normalizing","title":"normflows: A PyTorch Package for Normalizing Flows","date":"2023-01-26","arxiv_id":"2302.12014","repositories_listed":1,"syntology":null},{"url":"/paper/rigid-body-flows-for-sampling-molecular","slug":"rigid-body-flows-for-sampling-molecular","title":"Rigid Body Flows for Sampling Molecular Crystal Structures","date":"2023-01-26","arxiv_id":"2301.11355","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/rigid-body-flows-for-sampling-molecular#ran","syntology_url":"https://syntology.ai/paper/2301.11355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11355"}},"official":{"repos":["noegroup/rigid-flows"],"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/towards-flexibility-and-interpretability-of","slug":"towards-flexibility-and-interpretability-of","title":"Towards Flexibility and Interpretability of Gaussian Process State-Space Model","date":"2023-01-21","arxiv_id":"2301.08843","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-quadrature-sequence-and","slug":"an-efficient-quadrature-sequence-and","title":"Projective Integral Updates for High-Dimensional Variational Inference","date":"2023-01-20","arxiv_id":"2301.08374","repositories_listed":1,"syntology":null},{"url":"/paper/a-probabilistic-framework-for-visual","slug":"a-probabilistic-framework-for-visual","title":"A Probabilistic Framework for Visual Localization in Ambiguous Scenes","date":"2023-01-05","arxiv_id":"2301.02086","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-segmentation-as-gaussion-process","slug":"interactive-segmentation-as-gaussion-process","title":"Interactive Segmentation As Gaussion Process Classification","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-scalable-gaussian-process","slug":"robust-and-scalable-gaussian-process","title":"Robust and Scalable Gaussian Process Regression and Its Applications","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/joint-information-extraction-with-cross-task","slug":"joint-information-extraction-with-cross-task","title":"Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field","date":"2022-12-17","arxiv_id":"2212.08929","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-posterior-approximation-with","slug":"bayesian-posterior-approximation-with","title":"Bayesian posterior approximation with stochastic ensembles","date":"2022-12-15","arxiv_id":"2212.08123","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-posterior-approximation-with#ran","syntology_url":"https://syntology.ai/paper/2212.08123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08123"}},"official":{"repos":["oleksandr-balabanov/stochastic-ensembles"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-label-correlations-for-ultra-fine","slug":"modeling-label-correlations-for-ultra-fine","title":"Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field","date":"2022-12-03","arxiv_id":"2212.01581","repositories_listed":1,"syntology":null},{"url":"/paper/deep-gaussian-processes-for-air-quality","slug":"deep-gaussian-processes-for-air-quality","title":"Deep Gaussian Processes for Air Quality Inference","date":"2022-11-18","arxiv_id":"2211.10174","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-phrase-based-sequence-to","slug":"hierarchical-phrase-based-sequence-to","title":"Hierarchical Phrase-based Sequence-to-Sequence Learning","date":"2022-11-15","arxiv_id":"2211.07906","repositories_listed":1,"syntology":null},{"url":"/paper/variational-augmentation-for-enhancing","slug":"variational-augmentation-for-enhancing","title":"Variational Augmentation for Enhancing Historical Document Image Binarization","date":"2022-11-12","arxiv_id":"2211.06581","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-variational-autoencoder-for-1","slug":"semi-supervised-variational-autoencoder-for-1","title":"Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors","date":"2022-11-11","arxiv_id":"2211.05979","repositories_listed":1,"syntology":null},{"url":"/paper/thompson-sampling-for-high-dimensional-sparse","slug":"thompson-sampling-for-high-dimensional-sparse","title":"Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits","date":"2022-11-11","arxiv_id":"2211.05964","repositories_listed":1,"syntology":null},{"url":"/paper/black-box-coreset-variational-inference","slug":"black-box-coreset-variational-inference","title":"Black-box Coreset Variational Inference","date":"2022-11-04","arxiv_id":"2211.02377","repositories_listed":1,"syntology":null},{"url":"/paper/deconfounded-imitation-learning","slug":"deconfounded-imitation-learning","title":"Deconfounding Imitation Learning with Variational Inference","date":"2022-11-04","arxiv_id":"2211.02667","repositories_listed":1,"syntology":null},{"url":"/paper/exact-manifold-gaussian-variational-bayes","slug":"exact-manifold-gaussian-variational-bayes","title":"Manifold Gaussian Variational Bayes on the Precision Matrix","date":"2022-10-26","arxiv_id":"2210.14598","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-pessimism-in-dynamic-treatment","slug":"optimizing-pessimism-in-dynamic-treatment","title":"Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach","date":"2022-10-26","arxiv_id":"2210.14420","repositories_listed":1,"syntology":null}],"record_sha256":"37e58750081e74e92bcae40862f2628b35e525bff94525e30d001b8527017d95","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}