{"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/2","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":2,"pages_in_order":9,"rows_per_page":100,"rows":[101,200],"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","next":"/method/variational-inference/papers/3","papers":[{"paper":"/paper/variational-inference-on-the-boolean","slug":"variational-inference-on-the-boolean","title":"Variational Inference on the Boolean Hypercube with the Quantum Entropy","date":"2024-11-06","arxiv_id":"2411.03759","n_code_links":1,"syntology":null},{"paper":null,"slug":"recursive-learning-of-asymptotic-variational","title":"Recursive Learning of Asymptotic Variational Objectives","date":"2024-11-04","arxiv_id":"2411.02217","n_code_links":0,"syntology":null},{"paper":null,"slug":"eigenvi-score-based-variational-inference","title":"EigenVI: score-based variational inference with orthogonal function expansions","date":"2024-10-31","arxiv_id":"2410.24054","n_code_links":0,"syntology":null},{"paper":"/paper/functional-gradient-flows-for-constrained","slug":"functional-gradient-flows-for-constrained","title":"Functional Gradient Flows for Constrained Sampling","date":"2024-10-30","arxiv_id":"2410.23170","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":4,"n_instrument":6,"unverified":2,"pointer_only":9,"phrase":"10 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ShiyueZhang66/Constrained-Functional-Gradient-Flow"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["community","official"]}}},{"paper":"/paper/batch-match-and-patch-low-rank-approximations","slug":"batch-match-and-patch-low-rank-approximations","title":"Batch, match, and patch: low-rank approximations for score-based variational inference","date":"2024-10-29","arxiv_id":"2410.22292","n_code_links":1,"syntology":null},{"paper":null,"slug":"simsiam-naming-game-a-unified-approach-for","title":"SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication","date":"2024-10-29","arxiv_id":"2410.21803","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-inference-for-pile-up-removal-at","title":"Variational inference for pile-up removal at hadron colliders with diffusion models","date":"2024-10-29","arxiv_id":"2410.22074","n_code_links":0,"syntology":null},{"paper":null,"slug":"likelihood-approximations-via-gaussian","title":"Likelihood approximations via Gaussian approximate inference","date":"2024-10-28","arxiv_id":"2410.20754","n_code_links":0,"syntology":null},{"paper":"/paper/a-prescriptive-theory-for-brain-like","slug":"a-prescriptive-theory-for-brain-like","title":"Brain-like variational inference","date":"2024-10-25","arxiv_id":"2410.19315","n_code_links":0,"syntology":{"ran":1,"of":6,"n_ran_checked":1,"n_instrument":0,"unverified":5,"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) · 5 unverified","official":null}},{"paper":null,"slug":"noise-aware-differentially-private","title":"Noise-Aware Differentially Private Variational Inference","date":"2024-10-25","arxiv_id":"2410.19371","n_code_links":0,"syntology":null},{"paper":null,"slug":"evolving-voices-based-on-temporal-poisson","title":"Evolving Voices Based on Temporal Poisson Factorisation","date":"2024-10-24","arxiv_id":"2410.18486","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-implicit-functional-gradient-flow","title":"Semi-Implicit Functional Gradient Flow for Efficient Sampling","date":"2024-10-23","arxiv_id":"2410.17935","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-trust-region-method-for-graphical-stein","title":"A Trust-Region Method for Graphical Stein Variational Inference","date":"2024-10-21","arxiv_id":"2410.16195","n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-group-prior-into-variational","title":"Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction","date":"2024-10-19","arxiv_id":"2410.15098","n_code_links":0,"syntology":null},{"paper":null,"slug":"predictive-variational-inference-learn-the","title":"Predictive variational inference: Learn the predictively optimal posterior distribution","date":"2024-10-18","arxiv_id":"2410.14843","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-theoretical-perspective-on-mode-collapse-in","title":"A theoretical perspective on mode collapse in variational inference","date":"2024-10-17","arxiv_id":"2410.13300","n_code_links":0,"syntology":null},{"paper":"/paper/annealed-stein-variational-gradient-descent-1","slug":"annealed-stein-variational-gradient-descent-1","title":"Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion","date":"2024-10-17","arxiv_id":"2410.13249","n_code_links":1,"syntology":null},{"paper":"/paper/preference-diffusion-for-recommendation","slug":"preference-diffusion-for-recommendation","title":"Preference Diffusion for Recommendation","date":"2024-10-17","arxiv_id":"2410.13117","n_code_links":1,"syntology":{"ran":15,"of":19,"n_ran_checked":11,"n_instrument":4,"unverified":4,"pointer_only":19,"phrase":"15 ran (of which 5 constructed an object rather than computing a result; 11 with no instrument failure: 4 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["lswhim/preferdiff"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":5,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/counterfactual-generative-modeling-with","slug":"counterfactual-generative-modeling-with","title":"Counterfactual Generative Modeling with Variational Causal Inference","date":"2024-10-16","arxiv_id":"2410.12730","n_code_links":1,"syntology":{"ran":11,"of":13,"n_ran_checked":8,"n_instrument":3,"unverified":2,"pointer_only":0,"phrase":"11 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 5 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["yulun-rayn/variational-causal-inference"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-with-importance-weighted-variational","title":"Learning with Importance Weighted Variational Inference: Asymptotics for Gradient Estimators of the VR-IWAE Bound","date":"2024-10-15","arxiv_id":"2410.12035","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-lower-bounds-for-logistic-log","title":"Optimal lower bounds for logistic log-likelihoods","date":"2024-10-14","arxiv_id":"2410.10309","n_code_links":0,"syntology":null},{"paper":"/paper/variational-inference-in-location-scale","slug":"variational-inference-in-location-scale","title":"Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix","date":"2024-10-14","arxiv_id":"2410.11067","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-divergence-measures-for-training-gflownets","title":"On Divergence Measures for Training GFlowNets","date":"2024-10-12","arxiv_id":"2410.09355","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-reinforcement-learning-with-large","title":"Efficient Reinforcement Learning with Large Language Model Priors","date":"2024-10-10","arxiv_id":"2410.07927","n_code_links":0,"syntology":null},{"paper":null,"slug":"protect-before-generate-error-correcting","title":"Protect Before Generate: Error Correcting Codes within Discrete Deep Generative Models","date":"2024-10-10","arxiv_id":"2410.07840","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-estimation-and-tuning-free-rank","title":"Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors","date":"2024-10-08","arxiv_id":"2410.06329","n_code_links":0,"syntology":null},{"paper":null,"slug":"score-based-variational-inference-for-inverse","title":"Score-Based Variational Inference for Inverse Problems","date":"2024-10-08","arxiv_id":"2410.05646","n_code_links":0,"syntology":null},{"paper":"/paper/optimization-proxies-using-limited-labeled","slug":"optimization-proxies-using-limited-labeled","title":"Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach","date":"2024-10-04","arxiv_id":"2410.03085","n_code_links":1,"syntology":null},{"paper":"/paper/variational-bayes-gaussian-splatting","slug":"variational-bayes-gaussian-splatting","title":"Variational Bayes Gaussian Splatting","date":"2024-10-04","arxiv_id":"2410.03592","n_code_links":1,"syntology":null},{"paper":null,"slug":"stochastic-variance-reduced-gaussian","title":"Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold","date":"2024-10-03","arxiv_id":"2410.02490","n_code_links":0,"syntology":null},{"paper":"/paper/deep-generative-modeling-for-identification","slug":"deep-generative-modeling-for-identification","title":"Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems","date":"2024-10-02","arxiv_id":"2410.02079","n_code_links":1,"syntology":null},{"paper":"/paper/bayes-catsi-a-variational-bayesian-approach","slug":"bayes-catsi-a-variational-bayesian-approach","title":"Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation","date":"2024-10-01","arxiv_id":"2410.01847","n_code_links":1,"syntology":null},{"paper":null,"slug":"variational-source-channel-coding-for","title":"Variational Source-Channel Coding for Semantic Communication","date":"2024-09-26","arxiv_id":"2410.08222","n_code_links":0,"syntology":null},{"paper":null,"slug":"functional-stochastic-gradient-mcmc-for","title":"Functional Stochastic Gradient MCMC for Bayesian Neural Networks","date":"2024-09-25","arxiv_id":"2409.16632","n_code_links":0,"syntology":null},{"paper":null,"slug":"amortized-variational-inference-for-deep","title":"Amortized Variational Inference for Deep Gaussian Processes","date":"2024-09-18","arxiv_id":"2409.12301","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-mixed-effect-models-for-high","title":"Latent mixed-effect models for high-dimensional longitudinal data","date":"2024-09-17","arxiv_id":"2409.11008","n_code_links":0,"syntology":null},{"paper":null,"slug":"portfolio-stress-testing-and-value-at-risk","title":"Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions","date":"2024-09-12","arxiv_id":"2409.18970","n_code_links":0,"syntology":null},{"paper":"/paper/variational-search-distributions","slug":"variational-search-distributions","title":"Variational Search Distributions","date":"2024-09-10","arxiv_id":"2409.06142","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"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","official":{"repos":["csiro-funml/variationalsearch"],"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"]}}},{"paper":null,"slug":"half-vae-an-encoder-free-vae-to-bypass","title":"Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping","date":"2024-09-06","arxiv_id":"2409.04140","n_code_links":0,"syntology":null},{"paper":null,"slug":"sample-what-you-cant-compress","title":"Sample what you cant compress","date":"2024-09-04","arxiv_id":"2409.02529","n_code_links":0,"syntology":null},{"paper":"/paper/gradient-free-variational-learning-with","slug":"gradient-free-variational-learning-with","title":"Gradient-free variational learning with conditional mixture networks","date":"2024-08-29","arxiv_id":"2408.16429","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"pointer_only":4,"phrase":"1 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; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["versestech/cavi-cmn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-joint-learning-model-with-variational","slug":"a-joint-learning-model-with-variational","title":"A Joint Learning Model with Variational Interaction for Multilingual Program Translation","date":"2024-08-25","arxiv_id":"2408.14515","n_code_links":1,"syntology":null},{"paper":null,"slug":"decentralised-gradient-based-variational","title":"Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes","date":"2024-08-24","arxiv_id":"2408.13689","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-is-not-what-you-need-revisiting","title":"Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification","date":"2024-08-18","arxiv_id":"2408.09449","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-quantification-in-alzheimer-s","title":"Uncertainty Quantification in Alzheimer's Disease Progression Modeling","date":"2024-08-13","arxiv_id":"2408.14478","n_code_links":0,"syntology":null},{"paper":"/paper/divide-and-conquer-predictive-coding-a","slug":"divide-and-conquer-predictive-coding-a","title":"Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm","date":"2024-08-11","arxiv_id":"2408.05834","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["esennesh/dcpc_paper","esennesh/ppc_experiments"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"variational-inference-failures-under-model","title":"Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks","date":"2024-08-10","arxiv_id":"2408.05496","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-non-negative-vae-the-generalized-gamma","title":"A Non-negative VAE:the Generalized Gamma Belief Network","date":"2024-08-06","arxiv_id":"2408.03388","n_code_links":0,"syntology":null},{"paper":"/paper/2408-00490","slug":"2408-00490","title":"Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation","date":"2024-08-01","arxiv_id":"2408.00490","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-00681","title":"Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification","date":"2024-08-01","arxiv_id":"2408.00681","n_code_links":0,"syntology":null},{"paper":"/paper/weak-neural-variational-inference-for-solving","slug":"weak-neural-variational-inference-for-solving","title":"Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography","date":"2024-07-30","arxiv_id":"2407.20697","n_code_links":1,"syntology":null},{"paper":null,"slug":"regularized-multi-decoder-ensemble-for-an","title":"Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network","date":"2024-07-26","arxiv_id":"2407.19082","n_code_links":0,"syntology":null},{"paper":null,"slug":"amortized-posterior-sampling-with-diffusion","title":"Amortized Posterior Sampling with Diffusion Prior Distillation","date":"2024-07-25","arxiv_id":"2407.17907","n_code_links":0,"syntology":null},{"paper":null,"slug":"gradient-based-inference-of-abstract-task","title":"Gradient-based inference of abstract task representations for generalization in neural networks","date":"2024-07-24","arxiv_id":"2407.17356","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-inducing-points-in-deep-gaussian","slug":"sparse-inducing-points-in-deep-gaussian","title":"Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference","date":"2024-07-24","arxiv_id":"2407.17033","n_code_links":1,"syntology":null},{"paper":"/paper/diffusion-prior-based-amortized-variational","slug":"diffusion-prior-based-amortized-variational","title":"Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems","date":"2024-07-23","arxiv_id":"2407.16125","n_code_links":2,"syntology":{"ran":22,"of":27,"n_ran_checked":14,"n_instrument":8,"unverified":5,"pointer_only":17,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 0 violated, 12 with no contract checked; 8 where Syntology's instrument failed) · 5 unverified","official":{"repos":["mlvlab/davi"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"revisiting-score-function-estimators-for-k","title":"Revisiting Score Function Estimators for $k$-Subset Sampling","date":"2024-07-22","arxiv_id":"2407.16058","n_code_links":0,"syntology":null},{"paper":"/paper/softcvi-contrastive-variational-inference","slug":"softcvi-contrastive-variational-inference","title":"SoftCVI: Contrastive variational inference with self-generated soft labels","date":"2024-07-22","arxiv_id":"2407.15687","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"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) · 0 unverified","official":{"repos":["danielward27/pyrox","danielward27/softcvi","danielward27/softcvi_validation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hyperspectral-unmixing-under-endmember","title":"Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework","date":"2024-07-20","arxiv_id":"2407.14899","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-graph-out-of-distribution","title":"Improving Graph Out-of-distribution Generalization on Real-world Data","date":"2024-07-14","arxiv_id":"2407.10204","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-inference-via-smoothed-particle","title":"Variational Inference via Smoothed Particle Hydrodynamics","date":"2024-07-12","arxiv_id":"2407.09186","n_code_links":0,"syntology":null},{"paper":null,"slug":"autoregressive-speech-synthesis-without","title":"Autoregressive Speech Synthesis without Vector Quantization","date":"2024-07-11","arxiv_id":"2407.08551","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-best-of-n-alignment","title":"Variational Best-of-N Alignment","date":"2024-07-08","arxiv_id":"2407.06057","n_code_links":0,"syntology":null},{"paper":null,"slug":"idiographic-personality-gaussian-process-for","title":"Idiographic Personality Gaussian Process for Psychological Assessment","date":"2024-07-06","arxiv_id":"2407.04970","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-variational-causal-discovery","slug":"scalable-variational-causal-discovery","title":"Scalable Variational Causal Discovery Unconstrained by Acyclicity","date":"2024-07-06","arxiv_id":"2407.04992","n_code_links":1,"syntology":null},{"paper":"/paper/randomized-physics-informed-neural-networks","slug":"randomized-physics-informed-neural-networks","title":"Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation","date":"2024-07-05","arxiv_id":"2407.04617","n_code_links":1,"syntology":null},{"paper":"/paper/variational-partial-group-convolutions-for","slug":"variational-partial-group-convolutions-for","title":"Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts","date":"2024-07-05","arxiv_id":"2407.04271","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":["yegonkim/partial_equiv"],"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":null,"slug":"scalable-multi-output-gaussian-processes-with","title":"Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference","date":"2024-07-02","arxiv_id":"2407.02476","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-rkhs-fourier-features-for","slug":"adaptive-rkhs-fourier-features-for","title":"Adaptive RKHS Fourier Features for Compositional Gaussian Process Models","date":"2024-07-01","arxiv_id":"2407.01856","n_code_links":1,"syntology":null},{"paper":"/paper/particle-semi-implicit-variational-inference","slug":"particle-semi-implicit-variational-inference","title":"Particle Semi-Implicit Variational Inference","date":"2024-06-30","arxiv_id":"2407.00649","n_code_links":2,"syntology":{"ran":4,"of":11,"n_ran_checked":3,"n_instrument":1,"unverified":7,"pointer_only":3,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["jenninglim/pvi","longinyu/sivism"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/torchtree-flexible-phylogenetic-model","slug":"torchtree-flexible-phylogenetic-model","title":"Torchtree: flexible phylogenetic model development and inference using PyTorch","date":"2024-06-26","arxiv_id":"2406.18044","n_code_links":2,"syntology":null},{"paper":null,"slug":"empirical-bayes-for-dynamic-bayesian-networks","title":"Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference","date":"2024-06-25","arxiv_id":"2406.17831","n_code_links":0,"syntology":null},{"paper":"/paper/tree-based-variational-inference-for-poisson","slug":"tree-based-variational-inference-for-poisson","title":"Tree-based variational inference for Poisson log-normal models","date":"2024-06-25","arxiv_id":"2406.17361","n_code_links":1,"syntology":null},{"paper":"/paper/what-type-of-inference-is-planning","slug":"what-type-of-inference-is-planning","title":"What type of inference is planning?","date":"2024-06-25","arxiv_id":"2406.17863","n_code_links":1,"syntology":null},{"paper":null,"slug":"causalmmm-learning-causal-structure-for","title":"CausalMMM: Learning Causal Structure for Marketing Mix Modeling","date":"2024-06-24","arxiv_id":"2406.16728","n_code_links":0,"syntology":null},{"paper":"/paper/vicatmix-variational-bayesian-clustering-and","slug":"vicatmix-variational-bayesian-clustering-and","title":"VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data","date":"2024-06-23","arxiv_id":"2406.16227","n_code_links":1,"syntology":null},{"paper":null,"slug":"probabilistic-programming-with-programmable","title":"Probabilistic Programming with Programmable Variational Inference","date":"2024-06-22","arxiv_id":"2406.15742","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-generation-with-learned-action-prior","title":"Video Generation with Learned Action Prior","date":"2024-06-20","arxiv_id":"2406.14436","n_code_links":0,"syntology":null},{"paper":null,"slug":"period-singer-integrating-periodic-and","title":"Period Singer: Integrating Periodic and Aperiodic Variational Autoencoders for Natural-Sounding End-to-End Singing Voice Synthesis","date":"2024-06-14","arxiv_id":"2406.09894","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessment-of-uncertainty-quantification-in","title":"Assessment of Uncertainty Quantification in Universal Differential Equations","date":"2024-06-13","arxiv_id":"2406.08853","n_code_links":0,"syntology":null},{"paper":null,"slug":"injective-flows-for-parametric-hypersurfaces","title":"Injective flows for star-like manifolds","date":"2024-06-13","arxiv_id":"2406.09116","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-elbos-a-large-scale-evaluation-of","slug":"beyond-elbos-a-large-scale-evaluation-of","title":"Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling","date":"2024-06-11","arxiv_id":"2406.07423","n_code_links":1,"syntology":{"ran":9,"of":10,"n_ran_checked":9,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["denisbless/variational_sampling_methods"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"convergence-rate-of-random-scan-coordinate","title":"Convergence rate of random scan Coordinate Ascent Variational Inference under log-concavity","date":"2024-06-11","arxiv_id":"2406.07292","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-mixture-learning-in-black-box","slug":"efficient-mixture-learning-in-black-box","title":"Efficient Mixture Learning in Black-Box Variational Inference","date":"2024-06-11","arxiv_id":"2406.07083","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":10,"n_instrument":1,"unverified":1,"pointer_only":12,"phrase":"11 ran (of which 8 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["okviman/efficient-mixtures"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":8,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"central-limit-theorem-for-bayesian-neural","title":"Central Limit Theorem for Bayesian Neural Network trained with Variational Inference","date":"2024-06-10","arxiv_id":"2406.09048","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-full-waveform-inversion-with-deep","title":"Stochastic full waveform inversion with deep generative prior for uncertainty quantification","date":"2024-06-07","arxiv_id":"2406.04859","n_code_links":0,"syntology":null},{"paper":null,"slug":"concurrent-training-and-layer-pruning-of-deep","title":"Concurrent Training and Layer Pruning of Deep Neural Networks","date":"2024-06-06","arxiv_id":"2406.04549","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-kl-divergence-for-well-defined","title":"Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks","date":"2024-06-06","arxiv_id":"2406.04317","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-inference-mixture-of-gaussians","title":"Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians","date":"2024-06-06","arxiv_id":"2406.04012","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-pseudo-marginal-methods-for-jet","title":"Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics","date":"2024-06-05","arxiv_id":"2406.03242","n_code_links":0,"syntology":null},{"paper":null,"slug":"you-only-accept-samples-once-fast-self","title":"You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference","date":"2024-06-05","arxiv_id":"2406.02838","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-stochastic-natural-gradient","title":"Understanding Stochastic Natural Gradient Variational Inference","date":"2024-06-04","arxiv_id":"2406.01870","n_code_links":0,"syntology":null},{"paper":"/paper/veni-vindy-vici-a-variational-reduced-order","slug":"veni-vindy-vici-a-variational-reduced-order","title":"VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification","date":"2024-05-31","arxiv_id":"2405.20905","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-diffusion-models-corruption-stage","title":"Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks","date":"2024-05-30","arxiv_id":"2405.19931","n_code_links":0,"syntology":null},{"paper":"/paper/kernel-semi-implicit-variational-inference","slug":"kernel-semi-implicit-variational-inference","title":"Kernel Semi-Implicit Variational Inference","date":"2024-05-29","arxiv_id":"2405.18997","n_code_links":2,"syntology":{"ran":14,"of":16,"n_ran_checked":9,"n_instrument":5,"unverified":2,"pointer_only":16,"phrase":"14 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","official":{"repos":["longinyu/hsivi"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"renyi-neural-processes","title":"Rényi Neural Processes","date":"2024-05-25","arxiv_id":"2405.15991","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-preference-oriented-diversity-model-based","title":"A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search","date":"2024-05-24","arxiv_id":"2405.15521","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-identification-of-temporally-causal","title":"On the Identification of Temporally Causal Representation with Instantaneous Dependence","date":"2024-05-24","arxiv_id":"2405.15325","n_code_links":0,"syntology":null},{"paper":"/paper/prodag-projection-induced-variational","slug":"prodag-projection-induced-variational","title":"ProDAG: Projected Variational Inference for Directed Acyclic Graphs","date":"2024-05-24","arxiv_id":"2405.15167","n_code_links":1,"syntology":null},{"paper":"/paper/bayesian-adaptive-calibration-and-optimal","slug":"bayesian-adaptive-calibration-and-optimal","title":"Bayesian Adaptive Calibration and Optimal Design","date":"2024-05-23","arxiv_id":"2405.14440","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":2,"n_instrument":1,"unverified":4,"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) · 4 unverified","official":{"repos":["csiro-funml/bacon"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}}],"record_sha256":"239adc5bc0bd27f2f02c3b389146f43f6a7d228fa5dbb04929a3580d8a2ec6b1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}