{"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":"/method/variational-inference/papers/3","list_of":"/method/variational-inference","method":"Variational Inference","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":3,"pages_in_order":9,"rows_per_page":100,"rows":[201,300],"of":846,"counts":{"archive_papers_tagged":846,"with_a_code_link":337,"where_syntology_ran_a_sample":110,"not_listed_spam_title":0,"listed":846,"listed_where_code_ran":110,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":88,"every_run_a_failure_of_syntologys_instrument":22,"listed_with_a_run_with_no_instrument_failure":88,"listed_every_run_a_failure_of_syntologys_instrument":22,"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":"/method/variational-inference","prev":"/method/variational-inference/papers/2","next":"/method/variational-inference/papers/4","papers":[{"paper":"/paper/differentiable-annealed-importance-sampling-1","slug":"differentiable-annealed-importance-sampling-1","title":"Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distribution","date":"2024-05-23","arxiv_id":"2405.14840","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jzenn/dais0"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/variational-delayed-policy-optimization","slug":"variational-delayed-policy-optimization","title":"Variational Delayed Policy Optimization","date":"2024-05-23","arxiv_id":"2405.14226","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":4,"n_instrument":2,"unverified":1,"pointer_only":7,"phrase":"6 ran (of which 0 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) · 1 unverified","official":{"repos":["qingyuanwunothing/vdpo"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/zero-inflation-in-the-multivariate-poisson","slug":"zero-inflation-in-the-multivariate-poisson","title":"Zero-inflation in the Multivariate Poisson Lognormal Family","date":"2024-05-23","arxiv_id":"2405.14711","n_code_links":1,"syntology":null},{"paper":null,"slug":"diversity-aware-sign-language-production","title":"Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder","date":"2024-05-16","arxiv_id":"2405.10423","n_code_links":0,"syntology":null},{"paper":null,"slug":"variance-control-for-black-box-variational","title":"Variance Control for Black Box Variational Inference Using The James-Stein Estimator","date":"2024-05-09","arxiv_id":"2405.05485","n_code_links":0,"syntology":null},{"paper":"/paper/aspire-iterative-amortized-posterior","slug":"aspire-iterative-amortized-posterior","title":"ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems","date":"2024-05-08","arxiv_id":"2405.05398","n_code_links":1,"syntology":null},{"paper":"/paper/variational-schrodinger-diffusion-models","slug":"variational-schrodinger-diffusion-models","title":"Variational Schrödinger Diffusion Models","date":"2024-05-08","arxiv_id":"2405.04795","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-amortized-gplvms-for-single-cell","title":"Scalable Amortized GPLVMs for Single Cell Transcriptomics Data","date":"2024-05-06","arxiv_id":"2405.03879","n_code_links":0,"syntology":null},{"paper":null,"slug":"few-sample-variational-inference-of-bayesian","title":"Few-sample Variational Inference of Bayesian Neural Networks with Arbitrary Nonlinearities","date":"2024-05-03","arxiv_id":"2405.02063","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-convergence-in-bayesian-few-shot","slug":"accelerating-convergence-in-bayesian-few-shot","title":"Accelerating Convergence in Bayesian Few-Shot Classification","date":"2024-05-02","arxiv_id":"2405.01507","n_code_links":1,"syntology":null},{"paper":"/paper/s-2-ac-energy-based-reinforcement-learning","slug":"s-2-ac-energy-based-reinforcement-learning","title":"S$^2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic","date":"2024-05-02","arxiv_id":"2405.00987","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"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","official":{"repos":["safamessaoud/s2ac-energy-based-rl-with-stein-soft-actor-critic"],"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"]}}},{"paper":"/paper/implicit-generative-prior-for-bayesian-neural","slug":"implicit-generative-prior-for-bayesian-neural","title":"Implicit Generative Prior for Bayesian Neural Networks","date":"2024-04-27","arxiv_id":"2404.18008","n_code_links":1,"syntology":null},{"paper":null,"slug":"utilizing-graph-generation-for-enhanced","title":"Utilizing Graph Generation for Enhanced Domain Adaptive Object Detection","date":"2024-04-23","arxiv_id":"2406.06535","n_code_links":0,"syntology":null},{"paper":"/paper/calibrating-bayesian-learning-via","slug":"calibrating-bayesian-learning-via","title":"Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference","date":"2024-04-17","arxiv_id":"2404.11350","n_code_links":1,"syntology":null},{"paper":"/paper/variational-bayesian-last-layers","slug":"variational-bayesian-last-layers","title":"Variational Bayesian Last Layers","date":"2024-04-17","arxiv_id":"2404.11599","n_code_links":2,"syntology":{"ran":11,"of":23,"n_ran_checked":6,"n_instrument":5,"unverified":12,"pointer_only":0,"phrase":"11 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 1 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 12 unverified","official":{"repos":["vectorinstitute/vbll"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/analytical-approximation-of-the-elbo-gradient","slug":"analytical-approximation-of-the-elbo-gradient","title":"Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem","date":"2024-04-16","arxiv_id":"2404.10550","n_code_links":1,"syntology":null},{"paper":null,"slug":"nonlinear-sparse-variational-bayesian","title":"Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control","date":"2024-04-15","arxiv_id":"2404.09519","n_code_links":0,"syntology":null},{"paper":null,"slug":"sampling-for-model-predictive-trajectory","title":"Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows","date":"2024-04-15","arxiv_id":"2404.09657","n_code_links":0,"syntology":null},{"paper":null,"slug":"extending-mean-field-variational-inference","title":"Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation","date":"2024-04-14","arxiv_id":"2404.09113","n_code_links":0,"syntology":null},{"paper":null,"slug":"convergence-of-coordinate-ascent-variational","title":"Convergence of coordinate ascent variational inference for log-concave measures via optimal transport","date":"2024-04-12","arxiv_id":"2404.08792","n_code_links":0,"syntology":null},{"paper":"/paper/trajpred-trajectory-prediction-with-region","slug":"trajpred-trajectory-prediction-with-region","title":"TrajPRed: Trajectory Prediction with Region-based Relation Learning","date":"2024-04-10","arxiv_id":"2404.06971","n_code_links":1,"syntology":null},{"paper":"/paper/variational-stochastic-gradient-descent-for","slug":"variational-stochastic-gradient-descent-for","title":"Variational Stochastic Gradient Descent for Deep Neural Networks","date":"2024-04-09","arxiv_id":"2404.06549","n_code_links":1,"syntology":null},{"paper":"/paper/vi-ood-a-unified-representation-learning","slug":"vi-ood-a-unified-representation-learning","title":"VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection","date":"2024-04-09","arxiv_id":"2404.06217","n_code_links":1,"syntology":null},{"paper":"/paper/preventing-model-collapse-in-gaussian-process","slug":"preventing-model-collapse-in-gaussian-process","title":"Preventing Model Collapse in Gaussian Process Latent Variable Models","date":"2024-04-02","arxiv_id":"2404.01697","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zhidilin/advisedgplvm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/senm-vae-semi-supervised-noise-modeling-with","slug":"senm-vae-semi-supervised-noise-modeling-with","title":"SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder","date":"2024-03-26","arxiv_id":"2403.17502","n_code_links":1,"syntology":null},{"paper":null,"slug":"federated-bayesian-deep-learning-the","title":"Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models","date":"2024-03-22","arxiv_id":"2403.15263","n_code_links":0,"syntology":null},{"paper":"/paper/an-ordering-of-divergences-for-variational","slug":"an-ordering-of-divergences-for-variational","title":"Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs","date":"2024-03-20","arxiv_id":"2403.13748","n_code_links":1,"syntology":null},{"paper":"/paper/neural-markov-random-field-for-stereo","slug":"neural-markov-random-field-for-stereo","title":"Neural Markov Random Field for Stereo Matching","date":"2024-03-17","arxiv_id":"2403.11193","n_code_links":1,"syntology":{"ran":12,"of":13,"n_ran_checked":9,"n_instrument":3,"unverified":1,"pointer_only":1,"phrase":"12 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["aeolusguan/NMRF"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"variational-inference-with-sequential-sample","title":"VISA: Variational Inference with Sequential Sample-Average Approximations","date":"2024-03-14","arxiv_id":"2403.09429","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonparametric-automatic-differentiation","title":"Nonparametric Automatic Differentiation Variational Inference with Spline Approximation","date":"2024-03-10","arxiv_id":"2403.06302","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-inference-of-parameters-in","title":"Variational Inference of Parameters in Opinion Dynamics Models","date":"2024-03-08","arxiv_id":"2403.05358","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-variational-gaussian-state-space","slug":"large-scale-variational-gaussian-state-space","title":"eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling","date":"2024-03-03","arxiv_id":"2403.01371","n_code_links":1,"syntology":{"ran":8,"of":14,"n_ran_checked":8,"n_instrument":0,"unverified":6,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["catniplab/xfads"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"language-guided-skill-learning-with-temporal","title":"Language-guided Skill Learning with Temporal Variational Inference","date":"2024-02-26","arxiv_id":"2402.16354","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-convergence-of-stein-variational","slug":"accelerating-convergence-of-stein-variational","title":"Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding","date":"2024-02-23","arxiv_id":"2402.15125","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-framework-for-variational-inference-of","title":"A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties","date":"2024-02-22","arxiv_id":"2402.14532","n_code_links":0,"syntology":null},{"paper":"/paper/batch-and-match-black-box-variational","slug":"batch-and-match-black-box-variational","title":"Batch and match: black-box variational inference with a score-based divergence","date":"2024-02-22","arxiv_id":"2402.14758","n_code_links":2,"syntology":{"ran":7,"of":10,"n_ran_checked":6,"n_instrument":1,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["modichirag/gsm-vi","roualdes/bridgestan"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"uncertainty-quantification-of-graph","title":"Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes","date":"2024-02-17","arxiv_id":"2402.11179","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-entropy-search-for-adjusting","title":"Variational Entropy Search for Adjusting Expected Improvement","date":"2024-02-17","arxiv_id":"2402.11345","n_code_links":0,"syntology":null},{"paper":"/paper/blackjax-composable-bayesian-inference-in-jax","slug":"blackjax-composable-bayesian-inference-in-jax","title":"BlackJAX: Composable Bayesian inference in JAX","date":"2024-02-16","arxiv_id":"2402.10797","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"pointer_only":1,"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","official":{"repos":["blackjax-devs/blackjax"],"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"]}}},{"paper":"/paper/training-bayesian-neural-networks-with-sparse","slug":"training-bayesian-neural-networks-with-sparse","title":"Training Bayesian Neural Networks with Sparse Subspace Variational Inference","date":"2024-02-16","arxiv_id":"2402.11025","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["ljb121002/ssvi"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"variational-continual-test-time-adaptation","title":"Variational Continual Test-Time Adaptation","date":"2024-02-13","arxiv_id":"2402.08182","n_code_links":0,"syntology":null},{"paper":"/paper/latent-variable-model-for-high-dimensional","slug":"latent-variable-model-for-high-dimensional","title":"Latent variable model for high-dimensional point process with structured missingness","date":"2024-02-08","arxiv_id":"2402.05758","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"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","official":{"repos":["sinelnikovmaxim/mpp-vae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/the-vampprior-mixture-model","slug":"the-vampprior-mixture-model","title":"The VampPrior Mixture Model","date":"2024-02-06","arxiv_id":"2402.04412","n_code_links":1,"syntology":null},{"paper":"/paper/variational-flow-models-flowing-in-your-style","slug":"variational-flow-models-flowing-in-your-style","title":"Variational Flow Models: Flowing in Your Style","date":"2024-02-05","arxiv_id":"2402.02977","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-differentiable-poglm-with-forward-backward","title":"A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing","date":"2024-02-02","arxiv_id":"2402.01263","n_code_links":0,"syntology":null},{"paper":"/paper/bayesian-deep-learning-for-remaining-useful","slug":"bayesian-deep-learning-for-remaining-useful","title":"Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent","date":"2024-02-02","arxiv_id":"2402.01098","n_code_links":1,"syntology":null},{"paper":"/paper/self-attention-through-kernel-eigen-pair","slug":"self-attention-through-kernel-eigen-pair","title":"Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes","date":"2024-02-02","arxiv_id":"2402.01476","n_code_links":1,"syntology":null},{"paper":"/paper/activity-detection-for-massive-connectivity","slug":"activity-detection-for-massive-connectivity","title":"Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach","date":"2024-01-30","arxiv_id":"2401.16775","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-improved-variational-inference-for","title":"Towards Improved Variational Inference for Deep Bayesian Models","date":"2024-01-23","arxiv_id":"2401.12418","n_code_links":0,"syntology":null},{"paper":null,"slug":"safe-and-generalized-end-to-end-autonomous","title":"Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations","date":"2024-01-22","arxiv_id":"2401.11792","n_code_links":0,"syntology":null},{"paper":null,"slug":"provably-scalable-black-box-variational","title":"Provably Scalable Black-Box Variational Inference with Structured Variational Families","date":"2024-01-19","arxiv_id":"2401.10989","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-nonparametric-tensor-decomposition","slug":"efficient-nonparametric-tensor-decomposition","title":"Efficient Nonparametric Tensor Decomposition for Binary and Count Data","date":"2024-01-15","arxiv_id":"2401.07711","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 1 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) · 1 unverified","official":{"repos":["taozerui/gptd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scalable-and-efficient-methods-for","title":"Scalable and Efficient Methods for Uncertainty Estimation and Reduction in Deep Learning","date":"2024-01-13","arxiv_id":"2401.07145","n_code_links":0,"syntology":null},{"paper":"/paper/dualvae-dual-disentangled-variational","slug":"dualvae-dual-disentangled-variational","title":"DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation","date":"2024-01-10","arxiv_id":"2401.04914","n_code_links":1,"syntology":null},{"paper":"/paper/vi-pann-harnessing-transfer-learning-and","slug":"vi-pann-harnessing-transfer-learning-and","title":"VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition","date":"2024-01-10","arxiv_id":"2401.05531","n_code_links":1,"syntology":null},{"paper":null,"slug":"text-video-retrieval-via-variational-multi","title":"Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks","date":"2024-01-06","arxiv_id":"2401.03177","n_code_links":0,"syntology":null},{"paper":null,"slug":"complementary-information-mutual-learning-for","title":"Complementary Information Mutual Learning for Multimodality Medical Image Segmentation","date":"2024-01-05","arxiv_id":"2401.02717","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-variational-inference-diffusion","slug":"diffusion-variational-inference-diffusion","title":"Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors","date":"2024-01-05","arxiv_id":"2401.02739","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-outlier-detection-using-random","slug":"unsupervised-outlier-detection-using-random","title":"Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures","date":"2024-01-01","arxiv_id":"2401.00773","n_code_links":1,"syntology":null},{"paper":"/paper/continual-learning-via-sequential-function","slug":"continual-learning-via-sequential-function","title":"Continual Learning via Sequential Function-Space Variational Inference","date":"2023-12-28","arxiv_id":"2312.17210","n_code_links":1,"syntology":{"ran":8,"of":12,"n_ran_checked":2,"n_instrument":6,"unverified":4,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 4 unverified","official":{"repos":["timrudner/S-FSVI"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/tractable-function-space-variational","slug":"tractable-function-space-variational","title":"Tractable Function-Space Variational Inference in Bayesian Neural Networks","date":"2023-12-28","arxiv_id":"2312.17199","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["timrudner/fsvi"],"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"]}}},{"paper":"/paper/adaptive-message-passing-a-general-framework","slug":"adaptive-message-passing-a-general-framework","title":"Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching","date":"2023-12-27","arxiv_id":"2312.16560","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["nec-research/adaptive-message-passing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gad-pvi-a-general-accelerated-dynamic-weight","title":"GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework","date":"2023-12-27","arxiv_id":"2312.16429","n_code_links":0,"syntology":null},{"paper":"/paper/structured-probabilistic-coding","slug":"structured-probabilistic-coding","title":"Structured Probabilistic Coding","date":"2023-12-21","arxiv_id":"2312.13933","n_code_links":1,"syntology":null},{"paper":"/paper/partially-factorized-variational-inference","slug":"partially-factorized-variational-inference","title":"Partially factorized variational inference for high-dimensional mixed models","date":"2023-12-20","arxiv_id":"2312.13148","n_code_links":2,"syntology":null},{"paper":null,"slug":"robust-node-representation-learning-via-graph","title":"Robust Node Representation Learning via Graph Variational Diffusion Networks","date":"2023-12-18","arxiv_id":"2312.10903","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-model-selection-via-mean-field","title":"Bayesian Model Selection via Mean-Field Variational Approximation","date":"2023-12-17","arxiv_id":"2312.10607","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-state-estimation-and-noise","title":"Joint State Estimation and Noise Identification Based on Variational Optimization","date":"2023-12-15","arxiv_id":"2312.09585","n_code_links":0,"syntology":null},{"paper":"/paper/the-gaussian-linear-hidden-markov-model-a","slug":"the-gaussian-linear-hidden-markov-model-a","title":"The Gaussian-Linear Hidden Markov model: a Python package","date":"2023-12-12","arxiv_id":"2312.07151","n_code_links":1,"syntology":null},{"paper":null,"slug":"wise-full-waveform-variational-inference-via","title":"WISE: full-Waveform variational Inference via Subsurface Extensions","date":"2023-12-11","arxiv_id":"2401.06230","n_code_links":0,"syntology":null},{"paper":"/paper/ensemble-kalman-filtering-aided-variational","slug":"ensemble-kalman-filtering-aided-variational","title":"Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference","date":"2023-12-10","arxiv_id":"2312.05910","n_code_links":2,"syntology":null},{"paper":null,"slug":"sparse-variational-student-t-processes","title":"Sparse Variational Student-t Processes","date":"2023-12-09","arxiv_id":"2312.05568","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-bayesian-estimation-in-sensor","title":"Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities","date":"2023-12-02","arxiv_id":"2312.01227","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-parameter-free-robust-learning-using","slug":"adaptive-parameter-free-robust-learning-using","title":"Adaptive Robust Learning using Latent Bernoulli Variables","date":"2023-12-01","arxiv_id":"2312.00585","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["akarakulev/rlvi"],"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"]}}},{"paper":null,"slug":"identifiable-feature-learning-for-spatial","title":"Identifiable Feature Learning for Spatial Data with Nonlinear ICA","date":"2023-11-28","arxiv_id":"2311.16849","n_code_links":0,"syntology":null},{"paper":"/paper/probabilistic-transformer-a-probabilistic","slug":"probabilistic-transformer-a-probabilistic","title":"Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation","date":"2023-11-26","arxiv_id":"2311.15211","n_code_links":1,"syntology":null},{"paper":null,"slug":"favour-fast-variance-operator-for-uncertainty","title":"Favour: FAst Variance Operator for Uncertainty Rating","date":"2023-11-21","arxiv_id":"2311.13036","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-neural-networks-a-min-max-game","title":"Bayesian Neural Networks: A Min-Max Game Framework","date":"2023-11-18","arxiv_id":"2311.11126","n_code_links":0,"syntology":null},{"paper":null,"slug":"informative-priors-improve-the-reliability-of","title":"Informative Priors Improve the Reliability of Multimodal Clinical Data Classification","date":"2023-11-17","arxiv_id":"2312.00794","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-knowledge-distillation-approach-for-sepsis","title":"A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series","date":"2023-11-16","arxiv_id":"2311.09566","n_code_links":0,"syntology":null},{"paper":null,"slug":"mean-field-variational-inference-with-the-tap","title":"Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models","date":"2023-11-14","arxiv_id":"2311.08442","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-gaussian-process-based-method-with-deep","title":"A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options","date":"2023-11-13","arxiv_id":"2311.07211","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-ode-with-factorized-prototypes-for","title":"PGODE: Towards High-quality System Dynamics Modeling","date":"2023-11-11","arxiv_id":"2311.06554","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-multi-rank-high-order-bayesian-robust","title":"Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization","date":"2023-11-10","arxiv_id":"2311.05888","n_code_links":0,"syntology":null},{"paper":"/paper/estimating-treatment-effects-from-single-arm","slug":"estimating-treatment-effects-from-single-arm","title":"Estimating treatment effects from single-arm trials via latent-variable modeling","date":"2023-11-06","arxiv_id":"2311.03002","n_code_links":1,"syntology":null},{"paper":"/paper/neural-structure-learning-with-stochastic","slug":"neural-structure-learning-with-stochastic","title":"Neural Structure Learning with Stochastic Differential Equations","date":"2023-11-06","arxiv_id":"2311.03309","n_code_links":0,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"forward-kh-2-divergence-based-variational","title":"Forward $χ^2$ Divergence Based Variational Importance Sampling","date":"2023-11-04","arxiv_id":"2311.02516","n_code_links":0,"syntology":null},{"paper":"/paper/uncertainty-quantification-in-multivariable","slug":"uncertainty-quantification-in-multivariable","title":"Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks","date":"2023-11-04","arxiv_id":"2311.02495","n_code_links":1,"syntology":null},{"paper":null,"slug":"disentangled-representation-learning-with-4","title":"Disentangled Representation Learning with Transmitted Information Bottleneck","date":"2023-11-03","arxiv_id":"2311.01686","n_code_links":0,"syntology":null},{"paper":null,"slug":"vigraph-self-supervised-learning-for-class","title":"VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification","date":"2023-11-02","arxiv_id":"2311.01191","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-summarization-with-normalizing-flows","slug":"boosting-summarization-with-normalizing-flows","title":"Boosting Summarization with Normalizing Flows and Aggressive Training","date":"2023-11-01","arxiv_id":"2311.00588","n_code_links":1,"syntology":null},{"paper":"/paper/diffusion-models-for-probabilistic-1","slug":"diffusion-models-for-probabilistic-1","title":"Diffusion models for probabilistic programming","date":"2023-11-01","arxiv_id":"2311.00474","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-variational-inference-for","slug":"rethinking-variational-inference-for","title":"Rethinking Variational Inference for Probabilistic Programs with Stochastic Support","date":"2023-11-01","arxiv_id":"2311.00594","n_code_links":1,"syntology":{"ran":11,"of":17,"n_ran_checked":10,"n_instrument":1,"unverified":6,"pointer_only":17,"phrase":"11 ran (of which 9 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["treigerm/sdvi_neurips"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/bridging-the-gap-between-variational","slug":"bridging-the-gap-between-variational","title":"Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows","date":"2023-10-31","arxiv_id":"2310.20090","n_code_links":1,"syntology":null},{"paper":null,"slug":"introducing-instance-label-correlation-in","title":"Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images","date":"2023-10-30","arxiv_id":"2310.19359","n_code_links":0,"syntology":null},{"paper":null,"slug":"dysurv-dynamic-deep-learning-model-for","title":"DySurv: dynamic deep learning model for survival analysis with conditional variational inference","date":"2023-10-28","arxiv_id":"2310.18681","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-transformed-gaussian-processes","title":"Deep Transformed Gaussian Processes","date":"2023-10-27","arxiv_id":"2310.18230","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-semi-implicit-variational-1","slug":"hierarchical-semi-implicit-variational-1","title":"Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration","date":"2023-10-26","arxiv_id":"2310.17153","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["longinyu/hsivi"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-statistical-thermodynamics-of-generative","title":"The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability","date":"2023-10-26","arxiv_id":"2310.17467","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-domain-invariant-learning-via","title":"Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions","date":"2023-10-25","arxiv_id":"2310.16277","n_code_links":0,"syntology":null}],"record_sha256":"69ec71bae9483b55f567997c6cce033113d14143a47fa7ddf788321c72d2e4b1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}