{"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/density-estimation/papers/9","list_of":"/task/density-estimation","task":"Density Estimation","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":9,"pages_in_order":14,"rows_per_page":100,"rows":[801,900],"of":1394,"counts":{"archive_papers_tagged":1394,"with_a_code_link":498,"where_syntology_ran_a_sample":140,"not_listed_spam_title":0,"listed":1394,"listed_where_code_ran":140,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":118,"every_run_a_failure_of_syntologys_instrument":22,"listed_with_a_run_with_no_instrument_failure":118,"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":"/task/density-estimation","prev":"/task/density-estimation/papers/8","next":"/task/density-estimation/papers/10","papers":[{"url":null,"slug":"a-neural-mean-embedding-approach-for-back","title":"A Neural Mean Embedding Approach for Back-door and Front-door Adjustment","date":"2022-10-12","arxiv_id":"2210.06610","repositories_listed":0,"syntology":null},{"url":null,"slug":"modular-flows-differential-molecular","title":"Modular Flows: Differential Molecular Generation","date":"2022-10-12","arxiv_id":"2210.06032","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-mismatch-between-marginal-and","title":"Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression","date":"2022-10-12","arxiv_id":"2210.06596","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustify-transformers-with-robust-kernel","title":"Designing Robust Transformers using Robust Kernel Density Estimation","date":"2022-10-11","arxiv_id":"2210.05794","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-trajectory-based-density-estimation","title":"Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications","date":"2022-10-01","arxiv_id":"2210.00343","repositories_listed":0,"syntology":null},{"url":null,"slug":"butterflyflow-building-invertible-layers-with","title":"ButterflyFlow: Building Invertible Layers with Butterfly Matrices","date":"2022-09-28","arxiv_id":"2209.13774","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-constrained-continuous-probability","title":"Sampling Constrained Continuous Probability Distributions: A Review","date":"2022-09-26","arxiv_id":"2209.12403","repositories_listed":0,"syntology":null},{"url":"/paper/segmentation-of-patchy-areas-in-biomedical","slug":"segmentation-of-patchy-areas-in-biomedical","title":"Segmentation of patchy areas in biomedical images based on local edge density estimation","date":"2022-09-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-anomaly-detection","title":"Collaborative Anomaly Detection","date":"2022-09-20","arxiv_id":"2209.09923","repositories_listed":0,"syntology":null},{"url":null,"slug":"case-studies-for-computing-density-of","title":"Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning","date":"2022-09-16","arxiv_id":"2209.08073","repositories_listed":0,"syntology":null},{"url":null,"slug":"dataset-inference-for-self-supervised-models","title":"Dataset Inference for Self-Supervised Models","date":"2022-09-16","arxiv_id":"2209.09024","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-healing-the-blindness-of-score","title":"Towards Healing the Blindness of Score Matching","date":"2022-09-15","arxiv_id":"2209.07396","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-complexity-bounds-for-learning-high","title":"Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes","date":"2022-09-09","arxiv_id":"2209.05953","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-impact-of-model","title":"Investigating the Impact of Model Misspecification in Neural Simulation-based Inference","date":"2022-09-05","arxiv_id":"2209.01845","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-modeling-via-tree-tensor-network","title":"Generative Modeling via Tree Tensor Network States","date":"2022-09-03","arxiv_id":"2209.01341","repositories_listed":0,"syntology":null},{"url":null,"slug":"expert-opinion-elicitation-for-assisting-deep","title":"Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data","date":"2022-08-30","arxiv_id":"2208.14384","repositories_listed":0,"syntology":null},{"url":null,"slug":"pgnaa-spectral-classification-of-metal-with","title":"PGNAA Spectral Classification of Metal with Density Estimations","date":"2022-08-29","arxiv_id":"2208.13836","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-architectures-matter-on-the","title":"Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression","date":"2022-08-26","arxiv_id":"2208.12716","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-pca-for-flow-based-representation","title":"Neural PCA for Flow-Based Representation Learning","date":"2022-08-23","arxiv_id":"2208.10753","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-maintenance-of-kernel-density","title":"Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory","date":"2022-08-08","arxiv_id":"2208.03915","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-minimax-density-estimation-via-measure","title":"On minimax density estimation via measure transport","date":"2022-07-20","arxiv_id":"2207.10231","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-posterior-estimation-with","title":"Neural Posterior Estimation with Differentiable Simulators","date":"2022-07-12","arxiv_id":"2207.05636","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-deep-lossless-image-coding-with","title":"Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture","date":"2022-07-11","arxiv_id":"2207.05152","repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-diffusion-schrodinger-bridge","title":"Riemannian Diffusion Schrödinger Bridge","date":"2022-07-07","arxiv_id":"2207.03024","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-with-information","title":"Representation Learning with Information Theory for COVID-19 Detection","date":"2022-07-04","arxiv_id":"2207.01437","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-anomaly-detection-for-time-series","title":"Generative Anomaly Detection for Time Series Datasets","date":"2022-06-28","arxiv_id":"2206.14597","repositories_listed":0,"syntology":null},{"url":null,"slug":"led-latent-variable-based-estimation-of","title":"LED: Latent Variable-based Estimation of Density","date":"2022-06-23","arxiv_id":"2206.11563","repositories_listed":0,"syntology":null},{"url":null,"slug":"unerf-time-and-memory-conscious-u-shaped","title":"UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields","date":"2022-06-23","arxiv_id":"2206.11952","repositories_listed":0,"syntology":null},{"url":null,"slug":"lyapunov-density-models-constraining","title":"Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control","date":"2022-06-21","arxiv_id":"2206.10524","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-algebra-net-a-new-approach-generalizing","title":"$C^*$-algebra Net: A New Approach Generalizing Neural Network Parameters to $C^*$-algebra","date":"2022-06-20","arxiv_id":"2206.09513","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-out-of-distribution","title":"Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space","date":"2022-06-20","arxiv_id":"2206.09543","repositories_listed":0,"syntology":null},{"url":null,"slug":"density-estimation-with-autoregressive","title":"Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates","date":"2022-06-13","arxiv_id":"2206.06462","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovery-and-density-estimation-of-latent","title":"Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound","date":"2022-06-11","arxiv_id":"2206.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"mammodl-mammographic-breast-density","title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","date":"2022-06-11","arxiv_id":"2206.05575","repositories_listed":0,"syntology":null},{"url":null,"slug":"tropical-density-estimation-of-phylogenetic","title":"Tropical Density Estimation of Phylogenetic Trees","date":"2022-06-09","arxiv_id":"2206.04206","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-density-estimation-via-ncwfas","title":"Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata","date":"2022-06-08","arxiv_id":"2206.03923","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-manifold-learning-and-density","title":"Joint Manifold Learning and Density Estimation Using Normalizing Flows","date":"2022-06-07","arxiv_id":"2206.03293","repositories_listed":0,"syntology":null},{"url":null,"slug":"autm-flow-atomic-unrestricted-time-machine","title":"AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows","date":"2022-06-05","arxiv_id":"2206.02102","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcd-marginal-contrastive-discrimination-for","title":"MCD: Marginal Contrastive Discrimination for conditional density estimation","date":"2022-06-03","arxiv_id":"2206.01592","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise2noiseflow-realistic-camera-noise","title":"Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images","date":"2022-06-02","arxiv_id":"2206.01103","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-and-efficient-evolutionary","title":"An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization","date":"2022-05-31","arxiv_id":"2205.15884","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-transport-approach-for-selecting-a","title":"An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation","date":"2022-05-31","arxiv_id":"2206.01182","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-maximum-entropy-exploration","title":"k-Means Maximum Entropy Exploration","date":"2022-05-31","arxiv_id":"2205.15623","repositories_listed":0,"syntology":null},{"url":null,"slug":"exemplar-free-class-agnostic-counting","title":"Exemplar Free Class Agnostic Counting","date":"2022-05-27","arxiv_id":"2205.14212","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-probabilistic-forecasting-of","title":"Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows","date":"2022-05-27","arxiv_id":"2205.13826","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-consistency-with-dirichlet-process","title":"Clustering consistency with Dirichlet process mixtures","date":"2022-05-25","arxiv_id":"2205.12924","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-markov-model-for-near-term-railway","title":"A Novel Markov Model for Near-Term Railway Delay Prediction","date":"2022-05-21","arxiv_id":"2205.10682","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-mdod-training-end-to-end-multi-object","title":"End-to-End Multi-Object Detection with a Regularized Mixture Model","date":"2022-05-18","arxiv_id":"2205.08714","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-randomized-approximations-for-matrix","title":"Optimal Randomized Approximations for Matrix based Renyi's Entropy","date":"2022-05-16","arxiv_id":"2205.07426","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-trained-language-models-as-re-annotators","title":"Pre-trained Language Models as Re-Annotators","date":"2022-05-11","arxiv_id":"2205.05368","repositories_listed":0,"syntology":null},{"url":null,"slug":"reproducing-kernels-and-new-approaches-in","title":"Reproducing Kernels and New Approaches in Compositional Data Analysis","date":"2022-05-02","arxiv_id":"2205.01158","repositories_listed":0,"syntology":null},{"url":null,"slug":"bona-fide-riesz-projections-for-density","title":"Bona fide Riesz projections for density estimation","date":"2022-04-28","arxiv_id":"2204.13606","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphical-residual-flows","title":"Graphical Residual Flows","date":"2022-04-23","arxiv_id":"2204.11846","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-nonparametric-estimation-under","title":"Distributed Nonparametric Estimation under Communication Constraints","date":"2022-04-21","arxiv_id":"2204.10373","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-entropy-density-estimation-from","title":"Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data","date":"2022-04-12","arxiv_id":"2204.05480","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-lwe-is-as-hard-as-lwe-applications","title":"Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures","date":"2022-04-06","arxiv_id":"2204.02550","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-adapt-to-domain-shifts-with-few","title":"Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection","date":"2022-04-05","arxiv_id":"2204.01905","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-dialect-density-estimation-for","title":"Automatic Dialect Density Estimation for African American English","date":"2022-04-03","arxiv_id":"2204.00967","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-smoothness-incorporating-low-rank-1","title":"Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation","date":"2022-04-02","arxiv_id":"2204.00930","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-super-polynomial-lower-bound-for-learning","title":"Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures","date":"2022-03-28","arxiv_id":"2203.15150","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimisation-free-classification-and-density","title":"Optimisation-free Classification and Density Estimation with Quantum Circuits","date":"2022-03-28","arxiv_id":"2203.14452","repositories_listed":0,"syntology":null},{"url":null,"slug":"gransformer-transformer-based-graph","title":"Gransformer: Transformer-based Graph Generation","date":"2022-03-25","arxiv_id":"2203.13655","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-perspective-on-probabilistic-image","title":"A new perspective on probabilistic image modeling","date":"2022-03-21","arxiv_id":"2203.11034","repositories_listed":0,"syntology":null},{"url":null,"slug":"strong-posterior-contraction-rates-via","title":"Strong posterior contraction rates via Wasserstein dynamics","date":"2022-03-21","arxiv_id":"2203.10754","repositories_listed":0,"syntology":null},{"url":null,"slug":"takde-temporal-adaptive-kernel-density","title":"TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation","date":"2022-03-15","arxiv_id":"2203.08317","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinear-isometric-manifold-learning-for","title":"Nonlinear Isometric Manifold Learning for Injective Normalizing Flows","date":"2022-03-08","arxiv_id":"2203.03934","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-word-adversarial-examples-in","title":"Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation","date":"2022-03-03","arxiv_id":"2203.01677","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-density-estimation-by-genetic","title":"Kernel Density Estimation by Genetic Algorithm","date":"2022-03-03","arxiv_id":"2203.01535","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-the-boundaries-normalizing-flows-for","title":"Testing the boundaries: Normalizing Flows for higher dimensional data sets","date":"2022-02-18","arxiv_id":"2202.09188","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-generation-with-continuous","title":"Point Cloud Generation with Continuous Conditioning","date":"2022-02-17","arxiv_id":"2202.08526","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-reconstruction-study-of-breast","title":"A multi-reconstruction study of breast density estimation using Deep Learning","date":"2022-02-16","arxiv_id":"2202.08238","repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-a-step-back-with-kcal-multi-class","title":"Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks","date":"2022-02-15","arxiv_id":"2202.07679","repositories_listed":0,"syntology":null},{"url":null,"slug":"principal-manifold-flows","title":"Principal Manifold Flows","date":"2022-02-14","arxiv_id":"2202.07037","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiclass-histogram-based-thresholding-using","title":"Multiclass histogram-based thresholding using kernel density estimation and scale-space representations","date":"2022-02-10","arxiv_id":"2202.04785","repositories_listed":0,"syntology":null},{"url":null,"slug":"police-text-analysis-topic-modeling-and","title":"Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation","date":"2022-02-08","arxiv_id":"2202.04176","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyber-resilience-for-marine-navigation-by","title":"Cyber-resilience for marine navigation by information fusion and change detection","date":"2022-02-01","arxiv_id":"2202.03268","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-conditional-random-coefficient","title":"Estimation of Conditional Random Coefficient Models using Machine Learning Techniques","date":"2022-01-20","arxiv_id":"2201.08366","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-word-adversarial-examples-in-nlp","title":"Detection of Word Adversarial Examples in NLP: Benchmark and Baseline via Robust Density Estimation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-non-classical-parameterization-for-density","title":"A Non-Classical Parameterization for Density Estimation Using Sample Moments","date":"2022-01-13","arxiv_id":"2201.04786","repositories_listed":0,"syntology":null},{"url":null,"slug":"lomar-a-local-defense-against-poisoning","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","date":"2022-01-08","arxiv_id":"2201.02873","repositories_listed":0,"syntology":null},{"url":null,"slug":"lumbar-bone-mineral-density-estimation-from","title":"Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling","date":"2022-01-05","arxiv_id":"2201.01838","repositories_listed":0,"syntology":null},{"url":null,"slug":"triangular-flows-for-generative-modeling","title":"Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates","date":"2021-12-31","arxiv_id":"2112.15595","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrated-and-sharp-uncertainties-in-deep","title":"Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation","date":"2021-12-14","arxiv_id":"2112.07184","repositories_listed":0,"syntology":null},{"url":null,"slug":"probability-density-estimation-based","title":"Probability Density Estimation Based Imitation Learning","date":"2021-12-13","arxiv_id":"2112.06746","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoregressive-quantile-flows-for-predictive-1","title":"Autoregressive Quantile Flows for Predictive Uncertainty Estimation","date":"2021-12-09","arxiv_id":"2112.04643","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sampling-based-circuit-for-optimal-decision","title":"A sampling-based circuit for optimal decision making","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"double-machine-learning-density-estimation","title":"Double Machine Learning Density Estimation for Local Treatment Effects with Instruments","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"characteristic-neural-ordinary-differential","title":"Characteristic Neural Ordinary Differential Equations","date":"2021-11-25","arxiv_id":"2111.13207","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-density-estimation","title":"Tree density estimation","date":"2021-11-23","arxiv_id":"2111.11971","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-bayesian-inverse-reinforcement","title":"Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation","date":"2021-11-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"expert-guided-symmetry-detection-in-markov","title":"Expert-Guided Symmetry Detection in Markov Decision Processes","date":"2021-11-19","arxiv_id":"2111.10297","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-adversarial-examples-in-nlp","title":"Detection of Adversarial Examples in NLP: Benchmark and Baseline via Robust Density Estimation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"osoa-one-shot-online-adaptation-of-deep","title":"OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression","date":"2021-11-02","arxiv_id":"2111.01662","repositories_listed":0,"syntology":null},{"url":null,"slug":"bounds-all-around-training-energy-based","title":"Bounds all around: training energy-based models with bidirectional bounds","date":"2021-11-01","arxiv_id":"2111.00929","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-uncertainty-quantification-in","title":"Data-driven Uncertainty Quantification in Computational Human Head Models","date":"2021-10-29","arxiv_id":"2110.15553","repositories_listed":0,"syntology":null},{"url":"/paper/pedenet-image-anomaly-localization-via-patch","slug":"pedenet-image-anomaly-localization-via-patch","title":"PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation","date":"2021-10-29","arxiv_id":"2110.15525","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-ellipsoid-specific-fitting-via","title":"Robust Ellipsoid-specific Fitting via Expectation Maximization","date":"2021-10-26","arxiv_id":"2110.13337","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-density-estimation-based-sampling-for","title":"Kernel density estimation-based sampling for neural network classification","date":"2021-10-25","arxiv_id":"2110.12644","repositories_listed":0,"syntology":null},{"url":null,"slug":"knothe-rosenblatt-transport-for-unsupervised","title":"Knothe-Rosenblatt transport for Unsupervised Domain Adaptation","date":"2021-10-06","arxiv_id":"2110.02716","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-riemannian-concave-potential-maps","title":"Implicit Riemannian Concave Potential Maps","date":"2021-10-04","arxiv_id":"2110.01288","repositories_listed":0,"syntology":null}],"record_sha256":"8730b0a6e7454f5fcfe821d3c42acb1d631ab8d4b0c3e94e5c154aad1e538a81","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}