{"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/regression-1/papers/22","list_of":"/task/regression-1","task":"regression","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":22,"pages_in_order":95,"rows_per_page":100,"rows":[2101,2200],"of":9424,"counts":{"archive_papers_tagged":9424,"with_a_code_link":2445,"where_syntology_ran_a_sample":449,"not_listed_spam_title":0,"listed":9424,"listed_where_code_ran":449,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":374,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":374,"listed_every_run_a_failure_of_syntologys_instrument":75,"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/regression-1","prev":"/task/regression-1/papers/21","next":"/task/regression-1/papers/23","papers":[{"url":"/paper/regression-and-classification-for-direction","slug":"regression-and-classification-for-direction","title":"Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks","date":"2019-04-17","arxiv_id":"1904.08452","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-regression-in-duration-analysis","slug":"distribution-regression-in-duration-analysis","title":"Distribution Regression in Duration Analysis: an Application to Unemployment Spells","date":"2019-04-12","arxiv_id":"1904.06185","repositories_listed":1,"syntology":null},{"url":"/paper/geometry-aware-maximum-likelihood-estimation","slug":"geometry-aware-maximum-likelihood-estimation","title":"Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension","date":"2019-04-12","arxiv_id":"1904.06151","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geometry-aware-maximum-likelihood-estimation#ran","syntology_url":"https://syntology.ai/paper/1904.06151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.06151"}},"official":null}},{"url":"/paper/curriculum-semi-supervised-segmentation","slug":"curriculum-semi-supervised-segmentation","title":"Curriculum semi-supervised segmentation","date":"2019-04-10","arxiv_id":"1904.05236","repositories_listed":1,"syntology":null},{"url":"/paper/know-your-boundaries-constraining-gaussian","slug":"know-your-boundaries-constraining-gaussian","title":"Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features","date":"2019-04-10","arxiv_id":"1904.05207","repositories_listed":1,"syntology":null},{"url":"/paper/mvf-net-multi-view-3d-face-morphable-model","slug":"mvf-net-multi-view-3d-face-morphable-model","title":"MVF-Net: Multi-View 3D Face Morphable Model Regression","date":"2019-04-09","arxiv_id":"1904.04473","repositories_listed":1,"syntology":null},{"url":"/paper/regression-concept-vectors-for-bidirectional","slug":"regression-concept-vectors-for-bidirectional","title":"Regression Concept Vectors for Bidirectional Explanations in Histopathology","date":"2019-04-09","arxiv_id":"1904.04520","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-measures-and-prediction-quality","slug":"uncertainty-measures-and-prediction-quality","title":"Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images","date":"2019-04-09","arxiv_id":"1904.04516","repositories_listed":1,"syntology":null},{"url":"/paper/crosslingual-document-embedding-as-reduced","slug":"crosslingual-document-embedding-as-reduced","title":"Crosslingual Document Embedding as Reduced-Rank Ridge Regression","date":"2019-04-08","arxiv_id":"1904.03922","repositories_listed":1,"syntology":null},{"url":"/paper/model-based-genetic-programming-with-gomea","slug":"model-based-genetic-programming-with-gomea","title":"Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions","date":"2019-04-03","arxiv_id":"1904.02050","repositories_listed":1,"syntology":null},{"url":"/paper/3dregnet-a-deep-neural-network-for-3d-point","slug":"3dregnet-a-deep-neural-network-for-3d-point","title":"3DRegNet: A Deep Neural Network for 3D Point Registration","date":"2019-04-02","arxiv_id":"1904.01701","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/3dregnet-a-deep-neural-network-for-3d-point#ran","syntology_url":"https://syntology.ai/paper/1904.01701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01701"}},"official":{"repos":["3DVisionISR/3DRegNet"],"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","unlocated"]}}},{"url":"/paper/probabilistic-forecasting-of-sensory-data","slug":"probabilistic-forecasting-of-sensory-data","title":"Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN","date":"2019-03-29","arxiv_id":"1903.12549","repositories_listed":1,"syntology":null},{"url":"/paper/symbolic-regression-for-constructing-analytic","slug":"symbolic-regression-for-constructing-analytic","title":"Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression","date":"2019-03-27","arxiv_id":"1903.11483","repositories_listed":1,"syntology":null},{"url":"/paper/localized-linear-regression-in-networked-data","slug":"localized-linear-regression-in-networked-data","title":"Localized Linear Regression in Networked Data","date":"2019-03-26","arxiv_id":"1903.11178","repositories_listed":1,"syntology":null},{"url":"/paper/susi-supervised-self-organizing-maps-for","slug":"susi-supervised-self-organizing-maps-for","title":"SuSi: Supervised Self-Organizing Maps for Regression and Classification in Python","date":"2019-03-26","arxiv_id":"1903.11114","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-distributed-ridge-regression-in-high","slug":"one-shot-distributed-ridge-regression-in-high","title":"WONDER: Weighted one-shot distributed ridge regression in high dimensions","date":"2019-03-22","arxiv_id":"1903.09321","repositories_listed":1,"syntology":null},{"url":"/paper/fisher-discriminative-least-square-regression","slug":"fisher-discriminative-least-square-regression","title":"Fisher Discriminative Least Squares Regression for Image Classification","date":"2019-03-19","arxiv_id":"1903.07833","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-sets-in-multiple-instance","slug":"learning-with-sets-in-multiple-instance","title":"Learning with Sets in Multiple Instance Regression Applied to Remote Sensing","date":"2019-03-18","arxiv_id":"1903.07745","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-limitations-of-cnn-based","slug":"understanding-the-limitations-of-cnn-based","title":"Understanding the Limitations of CNN-based Absolute Camera Pose Regression","date":"2019-03-18","arxiv_id":"1903.07504","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-the-limitations-of-cnn-based#ran","syntology_url":"https://syntology.ai/paper/1903.07504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07504"}},"official":{"repos":["tsattler/understanding_apr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-distribution-regression","slug":"deep-distribution-regression","title":"Deep Distribution Regression","date":"2019-03-14","arxiv_id":"1903.06023","repositories_listed":1,"syntology":null},{"url":"/paper/fair-logistic-regression-an-adversarial","slug":"fair-logistic-regression-an-adversarial","title":"Fairness for Robust Log Loss Classification","date":"2019-03-10","arxiv_id":"1903.03910","repositories_listed":1,"syntology":null},{"url":"/paper/rates-of-convergence-for-sparse-variational","slug":"rates-of-convergence-for-sparse-variational","title":"Rates of Convergence for Sparse Variational Gaussian Process Regression","date":"2019-03-08","arxiv_id":"1903.03571","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rates-of-convergence-for-sparse-variational#ran","syntology_url":"https://syntology.ai/paper/1903.03571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03571"}},"official":null}},{"url":"/paper/safeguarded-dynamic-label-regression-for","slug":"safeguarded-dynamic-label-regression-for","title":"Safeguarded Dynamic Label Regression for Generalized Noisy Supervision","date":"2019-03-06","arxiv_id":"1903.02152","repositories_listed":1,"syntology":null},{"url":"/paper/what-to-expect-of-classifiers-reasoning-about","slug":"what-to-expect-of-classifiers-reasoning-about","title":"What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features","date":"2019-03-05","arxiv_id":"1903.01620","repositories_listed":1,"syntology":null},{"url":"/paper/tensor-variate-mixture-of-experts","slug":"tensor-variate-mixture-of-experts","title":"Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand","date":"2019-02-28","arxiv_id":"1902.11104","repositories_listed":1,"syntology":null},{"url":"/paper/cross-validation-in-sparse-linear-regression","slug":"cross-validation-in-sparse-linear-regression","title":"Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration","date":"2019-02-27","arxiv_id":"1902.10375","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-online-learning-with-kernels-for","slug":"efficient-online-learning-with-kernels-for","title":"Efficient online learning with kernels for adversarial large scale problems","date":"2019-02-26","arxiv_id":"1902.09917","repositories_listed":1,"syntology":null},{"url":"/paper/binscatter-regressions","slug":"binscatter-regressions","title":"Binscatter Regressions","date":"2019-02-25","arxiv_id":"1902.09615","repositories_listed":1,"syntology":null},{"url":"/paper/nonlinear-generalization-of-the-single-index","slug":"nonlinear-generalization-of-the-single-index","title":"Nonlinear generalization of the monotone single index model","date":"2019-02-24","arxiv_id":"1902.09024","repositories_listed":1,"syntology":null},{"url":"/paper/ares-and-mars-adversarial-and-mmd-minimizing","slug":"ares-and-mars-adversarial-and-mmd-minimizing","title":"AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs","date":"2019-02-22","arxiv_id":"1902.08480","repositories_listed":1,"syntology":null},{"url":"/paper/online-sampling-from-log-concave","slug":"online-sampling-from-log-concave","title":"Online Sampling from Log-Concave Distributions","date":"2019-02-21","arxiv_id":"1902.08179","repositories_listed":1,"syntology":null},{"url":"/paper/feature-relevance-bounds-for-ordinal","slug":"feature-relevance-bounds-for-ordinal","title":"Feature Relevance Bounds for Ordinal Regression","date":"2019-02-20","arxiv_id":"1902.07662","repositories_listed":1,"syntology":null},{"url":"/paper/a-generative-map-for-image-based-camera","slug":"a-generative-map-for-image-based-camera","title":"A Generative Map for Image-based Camera Localization","date":"2019-02-18","arxiv_id":"1902.11124","repositories_listed":1,"syntology":null},{"url":"/paper/towards-explainable-ai-significance-tests-for","slug":"towards-explainable-ai-significance-tests-for","title":"Significance Tests for Neural Networks","date":"2019-02-16","arxiv_id":"1902.06021","repositories_listed":1,"syntology":null},{"url":"/paper/interaction-transformation-evolutionary","slug":"interaction-transformation-evolutionary","title":"Interaction-Transformation Evolutionary Algorithm for Symbolic Regression","date":"2019-02-11","arxiv_id":"1902.03983","repositories_listed":1,"syntology":null},{"url":"/paper/ktboost-combined-kernel-and-tree-boosting","slug":"ktboost-combined-kernel-and-tree-boosting","title":"KTBoost: Combined Kernel and Tree Boosting","date":"2019-02-11","arxiv_id":"1902.03999","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-forest-a-concept-drift-aware-data","slug":"hybrid-forest-a-concept-drift-aware-data","title":"Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm","date":"2019-02-10","arxiv_id":"1902.03609","repositories_listed":1,"syntology":null},{"url":"/paper/censored-quantile-regression-forests","slug":"censored-quantile-regression-forests","title":"Censored Quantile Regression Forests","date":"2019-02-08","arxiv_id":"1902.03327","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-nonparametric-sampling-from","slug":"scalable-nonparametric-sampling-from","title":"Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap","date":"2019-02-08","arxiv_id":"1902.03175","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scalable-nonparametric-sampling-from#ran","syntology_url":"https://syntology.ai/paper/1902.03175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.03175"}},"official":{"repos":["edfong/npl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/concomitant-lasso-with-repetitions-clar","slug":"concomitant-lasso-with-repetitions-clar","title":"Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso","date":"2019-02-07","arxiv_id":"1902.02509","repositories_listed":1,"syntology":null},{"url":"/paper/face-alignment-using-a-3d-deeply-initialized","slug":"face-alignment-using-a-3d-deeply-initialized","title":"Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees","date":"2019-02-05","arxiv_id":"1902.01831","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-nonparametric-multiway-regression","slug":"bayesian-nonparametric-multiway-regression","title":"Bayesian nonparametric multiway regression for clustered binomial data","date":"2019-01-31","arxiv_id":"1901.11172","repositories_listed":1,"syntology":null},{"url":"/paper/contrasting-exploration-in-parameter-and","slug":"contrasting-exploration-in-parameter-and","title":"Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective","date":"2019-01-31","arxiv_id":"1901.11503","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-probabilistic-inference-for","slug":"end-to-end-probabilistic-inference-for","title":"End-to-End Probabilistic Inference for Nonstationary Audio Analysis","date":"2019-01-31","arxiv_id":"1901.11436","repositories_listed":1,"syntology":null},{"url":"/paper/improving-sgd-convergence-by-tracing-multiple","slug":"improving-sgd-convergence-by-tracing-multiple","title":"Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions","date":"2019-01-31","arxiv_id":"1901.11457","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspherical-prototype-networks","slug":"hyperspherical-prototype-networks","title":"Hyperspherical Prototype Networks","date":"2019-01-29","arxiv_id":"1901.10514","repositories_listed":1,"syntology":null},{"url":"/paper/fair-regression-for-health-care-spending","slug":"fair-regression-for-health-care-spending","title":"Fair Regression for Health Care Spending","date":"2019-01-28","arxiv_id":"1901.10566","repositories_listed":1,"syntology":null},{"url":"/paper/secure-multi-party-linear-regression-at","slug":"secure-multi-party-linear-regression-at","title":"Secure multi-party linear regression at plaintext speed","date":"2019-01-28","arxiv_id":"1901.09531","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-classification-using-multinomial","slug":"large-scale-classification-using-multinomial","title":"ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic Regression","date":"2019-01-27","arxiv_id":"1901.09450","repositories_listed":1,"syntology":null},{"url":"/paper/provably-efficient-rl-with-rich-observations","slug":"provably-efficient-rl-with-rich-observations","title":"Provably efficient RL with Rich Observations via Latent State Decoding","date":"2019-01-25","arxiv_id":"1901.09018","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/provably-efficient-rl-with-rich-observations#ran","syntology_url":"https://syntology.ai/paper/1901.09018","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09018"}},"official":{"repos":["Microsoft/StateDecoding"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-guided-symbolic-regression-with","slug":"neural-guided-symbolic-regression-with","title":"Neural-Guided Symbolic Regression with Asymptotic Constraints","date":"2019-01-23","arxiv_id":"1901.07714","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-gradient-trees","slug":"stochastic-gradient-trees","title":"Stochastic Gradient Trees","date":"2019-01-23","arxiv_id":"1901.07777","repositories_listed":1,"syntology":null},{"url":"/paper/the-autofeat-python-library-for-automatic","slug":"the-autofeat-python-library-for-automatic","title":"The autofeat Python Library for Automated Feature Engineering and Selection","date":"2019-01-22","arxiv_id":"1901.07329","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-autofeat-python-library-for-automatic#ran","syntology_url":"https://syntology.ai/paper/1901.07329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07329"}},"official":{"repos":["cod3licious/autofeat"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hybrid-coarse-fine-classification-for-head","slug":"hybrid-coarse-fine-classification-for-head","title":"Hybrid coarse-fine classification for head pose estimation","date":"2019-01-21","arxiv_id":"1901.06778","repositories_listed":1,"syntology":null},{"url":"/paper/a-bayesian-decision-tree-algorithm","slug":"a-bayesian-decision-tree-algorithm","title":"A Bayesian Decision Tree Algorithm","date":"2019-01-10","arxiv_id":"1901.03214","repositories_listed":1,"syntology":null},{"url":"/paper/neural-networks-versus-logistic-regression","slug":"neural-networks-versus-logistic-regression","title":"Neural networks versus Logistic regression for 30 days all-cause readmission prediction","date":"2018-12-22","arxiv_id":"1812.09549","repositories_listed":1,"syntology":null},{"url":"/paper/nonlinear-demixed-component-analysis-for","slug":"nonlinear-demixed-component-analysis-for","title":"Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem","date":"2018-12-19","arxiv_id":"1812.08238","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-topic-sentiment-preference-in-an-hdp","slug":"unifying-topic-sentiment-preference-in-an-hdp","title":"Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews","date":"2018-12-19","arxiv_id":"1812.07805","repositories_listed":1,"syntology":null},{"url":"/paper/taking-a-deeper-look-at-the-inverse","slug":"taking-a-deeper-look-at-the-inverse","title":"Taking a Deeper Look at the Inverse Compositional Algorithm","date":"2018-12-17","arxiv_id":"1812.06861","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/taking-a-deeper-look-at-the-inverse#ran","syntology_url":"https://syntology.ai/paper/1812.06861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.06861"}},"official":{"repos":["lvzhaoyang/DeeperInverseCompositionalAlgorithm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-processes-mixed-effect-models-for-deep","slug":"neural-processes-mixed-effect-models-for-deep","title":"Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data","date":"2018-12-12","arxiv_id":"1812.04998","repositories_listed":1,"syntology":null},{"url":"/paper/variational-bayesian-complex-network","slug":"variational-bayesian-complex-network","title":"Variational Bayesian Weighted Complex Network Reconstruction","date":"2018-12-11","arxiv_id":"1812.04369","repositories_listed":1,"syntology":null},{"url":"/paper/capturing-between-tasks-covariance-and","slug":"capturing-between-tasks-covariance-and","title":"Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models","date":"2018-12-10","arxiv_id":"1812.03662","repositories_listed":1,"syntology":null},{"url":"/paper/gspn-generative-shape-proposal-network-for-3d","slug":"gspn-generative-shape-proposal-network-for-3d","title":"GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud","date":"2018-12-08","arxiv_id":"1812.03320","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gspn-generative-shape-proposal-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/1812.03320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.03320"}},"official":{"repos":["ericyi/GSPN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/active-learning-for-non-parametric-regression","slug":"active-learning-for-non-parametric-regression","title":"Active Learning for Non-Parametric Regression Using Purely Random Trees","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ridge-regression-and-provable-deterministic","slug":"ridge-regression-and-provable-deterministic","title":"Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/intersectionality-multiple-group-fairness-in","slug":"intersectionality-multiple-group-fairness-in","title":"Intersectionality: Multiple Group Fairness in Expectation Constraints","date":"2018-11-25","arxiv_id":"1811.09960","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-propagation-through-cnns-for","slug":"confidence-propagation-through-cnns-for","title":"Confidence Propagation through CNNs for Guided Sparse Depth Regression","date":"2018-11-05","arxiv_id":"1811.01791","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-embed-probabilistic-structures","slug":"learning-to-embed-probabilistic-structures","title":"A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition","date":"2018-11-04","arxiv_id":"1811.01339","repositories_listed":1,"syntology":null},{"url":"/paper/frequentist-uncertainty-estimates-for-deep","slug":"frequentist-uncertainty-estimates-for-deep","title":"Single-Model Uncertainties for Deep Learning","date":"2018-11-02","arxiv_id":"1811.00908","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-error-meta-classification-in","slug":"prediction-error-meta-classification-in","title":"Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities","date":"2018-11-01","arxiv_id":"1811.00648","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/prediction-error-meta-classification-in#ran","syntology_url":"https://syntology.ai/paper/1811.00648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.00648"}},"official":{"repos":["mrottmann/MetaSeg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-highly-accurate-and-stable-face","slug":"towards-highly-accurate-and-stable-face","title":"Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos","date":"2018-11-01","arxiv_id":"1811.00342","repositories_listed":1,"syntology":null},{"url":"/paper/on-fast-leverage-score-sampling-and-optimal","slug":"on-fast-leverage-score-sampling-and-optimal","title":"On Fast Leverage Score Sampling and Optimal Learning","date":"2018-10-31","arxiv_id":"1810.13258","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-fast-leverage-score-sampling-and-optimal#ran","syntology_url":"https://syntology.ai/paper/1810.13258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.13258"}},"official":{"repos":["LCSL/bless"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-gaussian-processes-by-minimizing-pac","slug":"learning-gaussian-processes-by-minimizing-pac","title":"Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds","date":"2018-10-29","arxiv_id":"1810.12263","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-gaussian-processes-by-minimizing-pac#ran","syntology_url":"https://syntology.ai/paper/1810.12263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12263"}},"official":{"repos":["boschresearch/PAC_GP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-gaussian-process-regression-with","slug":"scaling-gaussian-process-regression-with","title":"Scaling Gaussian Process Regression with Derivatives","date":"2018-10-29","arxiv_id":"1810.12283","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-logistic-regression-learns-all","slug":"sparse-logistic-regression-learns-all","title":"Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models","date":"2018-10-28","arxiv_id":"1810.11905","repositories_listed":1,"syntology":null},{"url":"/paper/interpretability-is-harder-in-the-multiclass","slug":"interpretability-is-harder-in-the-multiclass","title":"Axiomatic Interpretability for Multiclass Additive Models","date":"2018-10-22","arxiv_id":"1810.09092","repositories_listed":1,"syntology":null},{"url":"/paper/a-regressive-convolution-neural-network-and","slug":"a-regressive-convolution-neural-network-and","title":"A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting","date":"2018-10-21","arxiv_id":"1810.08878","repositories_listed":1,"syntology":null},{"url":"/paper/hunting-for-discriminatory-proxies-in-linear","slug":"hunting-for-discriminatory-proxies-in-linear","title":"Hunting for Discriminatory Proxies in Linear Regression Models","date":"2018-10-16","arxiv_id":"1810.07155","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-theory-for-sketching-in-linear","slug":"a-new-theory-for-sketching-in-linear","title":"Asymptotics for Sketching in Least Squares Regression","date":"2018-10-14","arxiv_id":"1810.06089","repositories_listed":1,"syntology":null},{"url":"/paper/deep-clustering-on-the-link-between","slug":"deep-clustering-on-the-link-between","title":"Deep clustering: On the link between discriminative models and K-means","date":"2018-10-09","arxiv_id":"1810.04246","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-with-differential-gaussian","slug":"deep-learning-with-differential-gaussian","title":"Deep learning with differential Gaussian process flows","date":"2018-10-09","arxiv_id":"1810.04066","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/deep-learning-with-differential-gaussian#ran","syntology_url":"https://syntology.ai/paper/1810.04066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04066"}},"official":null}},{"url":"/paper/fast-context-adaptation-via-meta-learning","slug":"fast-context-adaptation-via-meta-learning","title":"Fast Context Adaptation via Meta-Learning","date":"2018-10-08","arxiv_id":"1810.03642","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fast-context-adaptation-via-meta-learning#ran","syntology_url":"https://syntology.ai/paper/1810.03642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03642"}},"official":{"repos":["lmzintgraf/cavia"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deconvolutional-time-series-regression-a","slug":"deconvolutional-time-series-regression-a","title":"Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/icon-interactive-conversational-memory","slug":"icon-interactive-conversational-memory","title":"ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/distributed-linear-regression-by-averaging","slug":"distributed-linear-regression-by-averaging","title":"Distributed linear regression by averaging","date":"2018-09-30","arxiv_id":"1810.00412","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-networks-for-estimation-and","slug":"deep-neural-networks-for-estimation-and","title":"Deep Neural Networks for Estimation and Inference","date":"2018-09-26","arxiv_id":"1809.09953","repositories_listed":1,"syntology":null},{"url":"/paper/mobileface-3d-face-reconstruction-with","slug":"mobileface-3d-face-reconstruction-with","title":"MobileFace: 3D Face Reconstruction with Efficient CNN Regression","date":"2018-09-24","arxiv_id":"1809.08809","repositories_listed":1,"syntology":null},{"url":"/paper/image-super-resolution-via-deterministic","slug":"image-super-resolution-via-deterministic","title":"Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification","date":"2018-09-18","arxiv_id":"1809.06557","repositories_listed":1,"syntology":null},{"url":"/paper/an-integral-pose-regression-system-for-the","slug":"an-integral-pose-regression-system-for-the","title":"An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge","date":"2018-09-17","arxiv_id":"1809.06079","repositories_listed":1,"syntology":null},{"url":"/paper/on-line-learning-of-linear-dynamical-systems","slug":"on-line-learning-of-linear-dynamical-systems","title":"On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters","date":"2018-09-16","arxiv_id":"1809.05870","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-line-learning-of-linear-dynamical-systems#ran","syntology_url":"https://syntology.ai/paper/1809.05870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05870"}},"official":null}},{"url":"/paper/emo2vec-learning-generalized-emotion","slug":"emo2vec-learning-generalized-emotion","title":"Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training","date":"2018-09-12","arxiv_id":"1809.04505","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-mixtures-of-gaussian-process","slug":"learning-deep-mixtures-of-gaussian-process","title":"Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks","date":"2018-09-12","arxiv_id":"1809.04400","repositories_listed":1,"syntology":null},{"url":"/paper/a-deeply-initialized-coarse-to-fine-ensemble","slug":"a-deeply-initialized-coarse-to-fine-ensemble","title":"A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-regression-tracking-with-shrinkage-loss","slug":"deep-regression-tracking-with-shrinkage-loss","title":"Deep Regression Tracking with Shrinkage Loss","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rational-neural-networks-for-approximating","slug":"rational-neural-networks-for-approximating","title":"Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator","date":"2018-08-30","arxiv_id":"1808.10073","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-expectation-deep-joint-mean-and","slug":"beyond-expectation-deep-joint-mean-and","title":"Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems","date":"2018-08-27","arxiv_id":"1808.08798","repositories_listed":1,"syntology":null},{"url":"/paper/cola-decentralized-linear-learning","slug":"cola-decentralized-linear-learning","title":"COLA: Decentralized Linear Learning","date":"2018-08-13","arxiv_id":"1808.04883","repositories_listed":1,"syntology":null},{"url":"/paper/active-learning-for-regression-using-greedy","slug":"active-learning-for-regression-using-greedy","title":"Active Learning for Regression Using Greedy Sampling","date":"2018-08-08","arxiv_id":"1808.04245","repositories_listed":1,"syntology":null},{"url":"/paper/unbiased-implicit-variational-inference","slug":"unbiased-implicit-variational-inference","title":"Unbiased Implicit Variational Inference","date":"2018-08-06","arxiv_id":"1808.02078","repositories_listed":1,"syntology":null},{"url":"/paper/diverse-conditional-image-generation-by","slug":"diverse-conditional-image-generation-by","title":"Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes","date":"2018-08-03","arxiv_id":"1808.01121","repositories_listed":1,"syntology":null}],"record_sha256":"6df956eb2c4a311a8f2a4c1683f678805afa9a639a24f64c295ecec83f9efe03","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}