{"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/bayesian-inference/papers/18","list_of":"/task/bayesian-inference","task":"Bayesian 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":18,"pages_in_order":23,"rows_per_page":100,"rows":[1701,1800],"of":2226,"counts":{"archive_papers_tagged":2226,"with_a_code_link":747,"where_syntology_ran_a_sample":164,"not_listed_spam_title":0,"listed":2226,"listed_where_code_ran":164,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":135,"every_run_a_failure_of_syntologys_instrument":29,"listed_with_a_run_with_no_instrument_failure":135,"listed_every_run_a_failure_of_syntologys_instrument":29,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/bayesian-inference","prev":"/task/bayesian-inference/papers/17","next":"/task/bayesian-inference/papers/19","papers":[{"url":null,"slug":"neural-permutation-processes","title":"Neural Permutation Processes","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stein-variational-gradient-descent-for","title":"Stein Variational Gradient Descent for Approximate Bayesian Computation","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-integration-of-multi-resolutional","title":"Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition","date":"2019-10-14","arxiv_id":"1910.05881","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-tracking-and-prediction-with","title":"Variational Tracking and Prediction with Generative Disentangled State-Space Models","date":"2019-10-14","arxiv_id":"1910.06205","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-triangular-mesh-mapping","title":"Stochastic triangular mesh mapping: A terrain mapping technique for autonomous mobile robots","date":"2019-10-08","arxiv_id":"1910.03644","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-scalable-uncertainty-estimation","title":"Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction","date":"2019-10-07","arxiv_id":"1910.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonasymptotic-estimates-for-stochastic","title":"Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization","date":"2019-10-04","arxiv_id":"1910.02008","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-surrogate-approach-to-efficient","title":"A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models","date":"2019-10-03","arxiv_id":"1910.01547","repositories_listed":0,"syntology":null},{"url":"/paper/bayesian-graph-convolution-lstm-for-skeleton","slug":"bayesian-graph-convolution-lstm-for-skeleton","title":"Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-audiovisual-activity","title":"Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-gaussian-processes-and-neural-networks-at","title":"Non-Gaussian processes and neural networks at finite widths","date":"2019-09-30","arxiv_id":"1910.00019","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-monte-carlo-bayesian-inference-1","title":"Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bamlss-a-lego-toolbox-for-flexible-bayesian","title":"bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)","date":"2019-09-25","arxiv_id":"1909.11784","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-meta-representations-of","title":"Gaussian Process Meta-Representations Of Neural Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-curves-for-deep-neural-networks-a-1","title":"Learning Curves for Deep Neural Networks: A field theory perspective","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-variational-inference","title":"Meta-Learning for Variational Inference","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-distributions-for-scalable-bayesian","title":"Mixture Distributions for Scalable Bayesian Inference","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-parameterization-of-gaussian-mean","title":"On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-uncertainty-with-gan-based-priors","title":"Quantifying uncertainty with GAN-based priors","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-the-variational-posterior-through","title":"Refining the variational posterior through iterative optimization","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-peek-into-the-unobservable-hidden-states","title":"A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series","date":"2019-09-24","arxiv_id":"1909.10957","repositories_listed":0,"syntology":null},{"url":null,"slug":"190909884","title":"Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control","date":"2019-09-21","arxiv_id":"1909.09884","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-estimate-labels-uncertainty-for","title":"Learn to Estimate Labels Uncertainty for Quality Assurance","date":"2019-09-17","arxiv_id":"1909.08058","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-uncertainty-estimation-for-hate","title":"Prediction Uncertainty Estimation for Hate Speech Classification","date":"2019-09-16","arxiv_id":"1909.07158","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-priors-for-bayesian-inference","title":"GAN priors for Bayesian inference","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-bayesian-synthetic-likelihood-with","title":"Efficient Bayesian synthetic likelihood with whitening transformations","date":"2019-09-11","arxiv_id":"1909.04857","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-safety-and-reliability-of","title":"Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing","date":"2019-08-19","arxiv_id":"1908.06540","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-making-in-dynamic-and-interactive","title":"Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control","date":"2019-08-12","arxiv_id":"1908.04005","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-large-scale-image","title":"Bayesian Inference for Large Scale Image Classification","date":"2019-08-09","arxiv_id":"1908.03491","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-error-bounds-for-deep","title":"Convergence Rates of Variational Inference in Sparse Deep Learning","date":"2019-08-09","arxiv_id":"1908.04847","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-incremental-inference-update-by-re","title":"Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm","date":"2019-08-06","arxiv_id":"1908.02002","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-deep-learning","title":"Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement","date":"2019-07-31","arxiv_id":"1907.13418","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-prediction-of-kinetic","title":"Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI","date":"2019-07-27","arxiv_id":"1907.11899","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-with-generative","title":"Bayesian Inference with Generative Adversarial Network Priors","date":"2019-07-22","arxiv_id":"1907.09987","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-data-driven-discovery-of-governing","title":"SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations","date":"2019-07-17","arxiv_id":"1907.07788","repositories_listed":0,"syntology":null},{"url":null,"slug":"concentration-of-the-matrix-valued-minimum","title":"Concentration of the matrix-valued minimum mean-square error in optimal Bayesian inference","date":"2019-07-15","arxiv_id":"1907.07103","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-synthesis-of-probabilistic-programs","title":"Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling","date":"2019-07-14","arxiv_id":"1907.06249","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-with-hierarchical","title":"Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data","date":"2019-07-08","arxiv_id":"1907.04240","repositories_listed":0,"syntology":null},{"url":null,"slug":"thompson-sampling-on-symmetric-stable-bandits","title":"Thompson Sampling on Symmetric $α$-Stable Bandits","date":"2019-07-08","arxiv_id":"1907.03821","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-mpc-for-bayesian-model","title":"Variational Inference MPC for Bayesian Model-based Reinforcement Learning","date":"2019-07-08","arxiv_id":"1907.04202","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approximate-bayesian-approach-to-surprise","title":"Learning in Volatile Environments with the Bayes Factor Surprise","date":"2019-07-05","arxiv_id":"1907.02936","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-cca-with-implicit-distributions","title":"Probabilistic CCA with Implicit Distributions","date":"2019-07-04","arxiv_id":"1907.02345","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-particle-based-approximations-of-the","title":"Adaptive particle-based approximations of the Gibbs posterior for inverse problems","date":"2019-07-02","arxiv_id":"1907.01551","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandit-learning-for-diversified-interactive","title":"Bandit Learning for Diversified Interactive Recommendation","date":"2019-07-01","arxiv_id":"1907.01647","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-of-spacecraft-pose-using","title":"Bayesian Inference of Spacecraft Pose using Particle Filtering","date":"2019-06-26","arxiv_id":"1906.11182","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-estimation-and-uncertainty","title":"Parameter Estimation and Uncertainty Quantification for Systems Biology Models","date":"2019-06-26","arxiv_id":"1906.11365","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-model-predictive-safety","title":"Probabilistic model predictive safety certification for learning-based control","date":"2019-06-25","arxiv_id":"1906.10417","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-calibration-for-convolutional","title":"Confidence Calibration for Convolutional Neural Networks Using Structured Dropout","date":"2019-06-23","arxiv_id":"1906.09551","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-curves-for-deep-neural-networks-a","title":"Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective","date":"2019-06-12","arxiv_id":"1906.05301","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-automatic-relevance-determination","title":"Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models","date":"2019-06-10","arxiv_id":"1906.03855","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-bayesian-myocardial-perfusion","title":"Hierarchical Bayesian myocardial perfusion quantification","date":"2019-06-06","arxiv_id":"1906.02540","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600309","title":"Sparse Bayesian Learning Approach for Discrete Signal Reconstruction","date":"2019-06-01","arxiv_id":"1906.00309","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-eye-tracking-with-bayesian","title":"Generalizing Eye Tracking With Bayesian Adversarial Learning","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-variational-auto-encoder-with","title":"Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators","date":"2019-05-30","arxiv_id":"1905.12867","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-polya-inverse-gamma","title":"Data Augementation with Polya Inverse Gamma","date":"2019-05-29","arxiv_id":"1905.12141","repositories_listed":0,"syntology":null},{"url":null,"slug":"switching-linear-dynamics-for-variational","title":"Switching Linear Dynamics for Variational Bayes Filtering","date":"2019-05-29","arxiv_id":"1905.12434","repositories_listed":0,"syntology":null},{"url":null,"slug":"walsh-hadamard-variational-inference-for","title":"Walsh-Hadamard Variational Inference for Bayesian Deep Learning","date":"2019-05-27","arxiv_id":"1905.11248","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-discovery-and-forecasting-in","title":"Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models","date":"2019-05-26","arxiv_id":"1905.10857","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-bayes-a-report-on-approaches-and","title":"Variational Bayes: A report on approaches and applications","date":"2019-05-26","arxiv_id":"1905.10744","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-bayesian-learning-over-graphs","title":"Decentralized Bayesian Learning over Graphs","date":"2019-05-24","arxiv_id":"1905.10466","repositories_listed":0,"syntology":null},{"url":null,"slug":"190511937","title":"Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting","date":"2019-05-23","arxiv_id":"1905.11937","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-langevin-sampling-with-birth","title":"Accelerating Langevin Sampling with Birth-death","date":"2019-05-23","arxiv_id":"1905.09863","repositories_listed":0,"syntology":null},{"url":null,"slug":"lr-glm-high-dimensional-bayesian-inference","title":"LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations","date":"2019-05-17","arxiv_id":"1905.07499","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-aware-imaging-system-ranking","title":"Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference","date":"2019-05-14","arxiv_id":"1905.05820","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-approximations-using-fisher","title":"Variational approximations using Fisher divergence","date":"2019-05-13","arxiv_id":"1905.05284","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-reconstruction-with-deep-neural","title":"Spectral Reconstruction with Deep Neural Networks","date":"2019-05-10","arxiv_id":"1905.04305","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-variational-framework-for","title":"A Latent Variational Framework for Stochastic Optimization","date":"2019-05-05","arxiv_id":"1905.01707","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-domain-adaptation","title":"Variational Domain Adaptation","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-acceleration-for-approximate","title":"Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout","date":"2019-04-29","arxiv_id":"1904.12904","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-bayesian-imitation-learning-with","title":"Few-Shot Bayesian Imitation Learning with Logical Program Policies","date":"2019-04-12","arxiv_id":"1904.06317","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-reconstruction-using","title":"Compressed sensing reconstruction using Expectation Propagation","date":"2019-04-10","arxiv_id":"1904.05777","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalization-bound-for-online-variational","title":"A Generalization Bound for Online Variational Inference","date":"2019-04-08","arxiv_id":"1904.03920","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-kikuchi-hierarchy-and-tensor-pca","title":"The Kikuchi Hierarchy and Tensor PCA","date":"2019-04-08","arxiv_id":"1904.03858","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-personalized-thermal-preferences-via","title":"Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints","date":"2019-03-21","arxiv_id":"1903.09094","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-mean-curvature","title":"Weighted Mean Curvature","date":"2019-03-17","arxiv_id":"1903.07189","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-armed-bandit-mcmc-with-applications","title":"A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior","date":"2019-03-13","arxiv_id":"1903.05726","repositories_listed":0,"syntology":null},{"url":null,"slug":"elements-of-sequential-monte-carlo","title":"Elements of Sequential Monte Carlo","date":"2019-03-12","arxiv_id":"1903.04797","repositories_listed":0,"syntology":null},{"url":null,"slug":"goal-directed-behavior-under-variational","title":"Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory","date":"2019-03-12","arxiv_id":"1903.04932","repositories_listed":0,"syntology":null},{"url":null,"slug":"embarrassingly-parallel-mcmc-using-deep","title":"Embarrassingly parallel MCMC using deep invertible transformations","date":"2019-03-11","arxiv_id":"1903.04556","repositories_listed":0,"syntology":null},{"url":null,"slug":"spiking-neural-network-on-neuromorphic","title":"Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM","date":"2019-03-06","arxiv_id":"1903.02504","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-object-based-perception-and","title":"Joint Perception and Control as Inference with an Object-based Implementation","date":"2019-03-04","arxiv_id":"1903.01385","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-system-architecture-utilizing-hybrid","title":"V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures","date":"2019-03-04","arxiv_id":"1903.01576","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximation-properties-of-variational-bayes","title":"Approximation Properties of Variational Bayes for Vector Autoregressions","date":"2019-03-02","arxiv_id":"1903.00617","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-gaussian-copula-abc","title":"Adaptive Gaussian Copula ABC","date":"2019-02-27","arxiv_id":"1902.10704","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-optimisation-assisted-gaussian","title":"Manifold Optimization Assisted Gaussian Variational Approximation","date":"2019-02-11","arxiv_id":"1902.03718","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stochastic-version-of-stein-variational","title":"A stochastic version of Stein Variational Gradient Descent for efficient sampling","date":"2019-02-09","arxiv_id":"1902.03394","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-pass-filtering-as-bayesian-inference","title":"Low-pass filtering as Bayesian inference","date":"2019-02-09","arxiv_id":"1902.03427","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-for-accurate","title":"A Bayesian Approach for Accurate Classification-Based Aggregates","date":"2019-02-06","arxiv_id":"1902.02412","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-asymptotic-results-for-langevin-monte","title":"Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference","date":"2019-02-04","arxiv_id":"1902.01373","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-uncertainty-quantification-with","title":"Predictive Uncertainty Quantification with Compound Density Networks","date":"2019-02-04","arxiv_id":"1902.01080","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-bayesian-detection-of-spike","title":"Sequential Bayesian Detection of Spike Activities from Fluorescence Observations","date":"2019-01-31","arxiv_id":"1901.11418","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-does-the-free-energy-principle-tell-us","title":"What does the free energy principle tell us about the brain?","date":"2019-01-23","arxiv_id":"1901.07945","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-combined-entropy-and-utility-based","title":"A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data","date":"2019-01-18","arxiv_id":"1901.06415","repositories_listed":0,"syntology":null},{"url":null,"slug":"theory-of-minds-understanding-behavior-in","title":"Theory of Minds: Understanding Behavior in Groups Through Inverse Planning","date":"2019-01-18","arxiv_id":"1901.06085","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-ensemble-classification-for","title":"Multi-modal Ensemble Classification for Generalized Zero Shot Learning","date":"2019-01-15","arxiv_id":"1901.04623","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-shrinkage-in-mixture-of-experts","title":"Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership","date":"2019-01-12","arxiv_id":"1809.04853","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-based-out-of-distribution","title":"Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning","date":"2019-01-08","arxiv_id":"1901.02219","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-you-trust-this-prediction-auditing","title":"Can You Trust This Prediction? Auditing Pointwise Reliability After Learning","date":"2019-01-02","arxiv_id":"1901.00403","repositories_listed":0,"syntology":null},{"url":null,"slug":"guess-whos-coming-and-whos-going-bringing","title":"Guess Who's Coming (and Who's Going): Bringing Perspective to the Rational Speech Acts Framework","date":"2019-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-learning-of-turbulent-flows-using","title":"Physics-Based Learning for Robotic Environmental Sensing","date":"2018-12-10","arxiv_id":"1812.03894","repositories_listed":0,"syntology":null}],"record_sha256":"3028b1652ed89e9916a10f7dab3d76b1b4085f01b968417f8e7f604700b4b7b4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}