{"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/84","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":84,"pages_in_order":95,"rows_per_page":100,"rows":[8301,8400],"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/83","next":"/task/regression-1/papers/85","papers":[{"url":null,"slug":"deep-head-pose-estimation-from-depth-data-for","title":"Deep Head Pose Estimation from Depth Data for In-car Automotive Applications","date":"2017-03-06","arxiv_id":"1703.01883","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-cost-sensitive","title":"Active Learning for Cost-Sensitive Classification","date":"2017-03-03","arxiv_id":"1703.01014","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robot-activities-from-first-person","title":"Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression","date":"2017-03-03","arxiv_id":"1703.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"encrypted-accelerated-least-squares","title":"Encrypted accelerated least squares regression","date":"2017-03-02","arxiv_id":"1703.00839","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-machine-learning-methods-to","title":"Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data","date":"2017-02-28","arxiv_id":"1703.02116","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-bayesian-learning-for-crowdsourced","title":"Iterative Bayesian Learning for Crowdsourced Regression","date":"2017-02-28","arxiv_id":"1702.08840","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-rates-for-classification-with","title":"Learning rates for classification with Gaussian kernels","date":"2017-02-28","arxiv_id":"1702.08701","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-categorical-attribute-transformation","title":"Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining","date":"2017-02-28","arxiv_id":"1702.08745","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-pseudo-likelihood-method-for","title":"An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation","date":"2017-02-27","arxiv_id":"1702.08320","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-update-on-statistical-boosting-in","title":"An update on statistical boosting in biomedicine","date":"2017-02-27","arxiv_id":"1702.08185","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-global-refinement-of","title":"Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment","date":"2017-02-27","arxiv_id":"1702.08481","repositories_listed":0,"syntology":null},{"url":null,"slug":"selection-of-training-populations-and-other","title":"Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm)","date":"2017-02-26","arxiv_id":"1702.08088","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-coordinate-wise-leading-eigenvector","title":"Efficient coordinate-wise leading eigenvector computation","date":"2017-02-25","arxiv_id":"1702.07834","repositories_listed":0,"syntology":null},{"url":null,"slug":"computationally-efficient-robust-estimation","title":"Computationally Efficient Robust Estimation of Sparse Functionals","date":"2017-02-24","arxiv_id":"1702.07709","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-rates-for-kernel-based-expectile","title":"Learning Rates for Kernel-Based Expectile Regression","date":"2017-02-24","arxiv_id":"1702.07552","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaptv-accurate-and-interpretable-low","title":"GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification","date":"2017-02-23","arxiv_id":"1702.07405","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-clustering-and-ensemble-of","title":"k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge","date":"2017-02-23","arxiv_id":"1702.07333","repositories_listed":0,"syntology":null},{"url":null,"slug":"sobolev-norm-learning-rates-for-regularized","title":"Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm","date":"2017-02-23","arxiv_id":"1702.07254","repositories_listed":0,"syntology":null},{"url":null,"slug":"delving-deeper-into-mooc-student-dropout","title":"Delving Deeper into MOOC Student Dropout Prediction","date":"2017-02-21","arxiv_id":"1702.06404","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-outliers-localized-logistic","title":"Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation","date":"2017-02-21","arxiv_id":"1702.06354","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-composite-least-squares-regression","title":"Stochastic Composite Least-Squares Regression with convergence rate O(1/n)","date":"2017-02-21","arxiv_id":"1702.06429","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-learning-approach-to-modal","title":"A Statistical Learning Approach to Modal Regression","date":"2017-02-20","arxiv_id":"1702.05960","repositories_listed":0,"syntology":null},{"url":null,"slug":"uniform-inference-for-high-dimensional","title":"Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores","date":"2017-02-20","arxiv_id":"1702.06209","repositories_listed":0,"syntology":null},{"url":null,"slug":"saga-and-restricted-strong-convexity","title":"SAGA and Restricted Strong Convexity","date":"2017-02-19","arxiv_id":"1702.05683","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-bayes-learning-of-stochastic","title":"Approximate Bayes learning of stochastic differential equations","date":"2017-02-17","arxiv_id":"1702.05390","repositories_listed":0,"syntology":null},{"url":"/paper/machine-learning-prediction-errors-better","slug":"machine-learning-prediction-errors-better","title":"Machine learning prediction errors better than DFT accuracy","date":"2017-02-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-surgery-duration-with-neural","title":"Predicting Surgery Duration with Neural Heteroscedastic Regression","date":"2017-02-17","arxiv_id":"1702.05386","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-equations-of-random-convex-functions","title":"Solving Equations of Random Convex Functions via Anchored Regression","date":"2017-02-17","arxiv_id":"1702.05327","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-concentration-inequality-for-the-excess","title":"A concentration inequality for the excess risk in least-squares regression with random design and heteroscedastic noise","date":"2017-02-16","arxiv_id":"1702.05063","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketched-ridge-regression-optimization","title":"Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging","date":"2017-02-16","arxiv_id":"1702.04837","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-ensembles-with-rule-structured-horseshoe","title":"Tree Ensembles with Rule Structured Horseshoe Regularization","date":"2017-02-16","arxiv_id":"1702.05008","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-regression-via-mutivariate-regression","title":"Robust Regression via Mutivariate Regression Depth","date":"2017-02-15","arxiv_id":"1702.04656","repositories_listed":0,"syntology":null},{"url":null,"slug":"coresets-for-kernel-regression","title":"Coresets for Kernel Regression","date":"2017-02-13","arxiv_id":"1702.03644","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-of-a-time-delay-reservoir-using","title":"Design of a Time Delay Reservoir Using Stochastic Logic: A Feasibility Study","date":"2017-02-13","arxiv_id":"1702.04265","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-configuration-networks","title":"Stochastic Configuration Networks: Fundamentals and Algorithms","date":"2017-02-10","arxiv_id":"1702.03180","repositories_listed":0,"syntology":null},{"url":null,"slug":"driver-drowsiness-estimation-from-eeg-signals","title":"Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)","date":"2017-02-09","arxiv_id":"1702.02901","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixing-an-error-in-caponnetto-and-de-vito","title":"Fixing an error in Caponnetto and de Vito (2007)","date":"2017-02-09","arxiv_id":"1702.02982","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fly-adaptation-of-regression-forests","title":"On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation","date":"2017-02-09","arxiv_id":"1702.02779","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-privileged-information-for-height","title":"Predicting Privileged Information for Height Estimation","date":"2017-02-09","arxiv_id":"1702.02709","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-optimal-estimation-and-confidence","title":"Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates","date":"2017-02-09","arxiv_id":"1702.02686","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-filtering-for-eeg-based-regression","title":"Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)","date":"2017-02-09","arxiv_id":"1702.02914","repositories_listed":0,"syntology":null},{"url":"/paper/region-ensemble-network-improving","slug":"region-ensemble-network-improving","title":"Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation","date":"2017-02-08","arxiv_id":"1702.02447","repositories_listed":0,"syntology":null},{"url":null,"slug":"printed-arabic-text-recognition-using-linear","title":"Printed Arabic Text Recognition using Linear and Nonlinear Regression","date":"2017-02-05","arxiv_id":"1702.01444","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-based-kriging-approximation","title":"Cluster-based Kriging Approximation Algorithms for Complexity Reduction","date":"2017-02-04","arxiv_id":"1702.01313","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-subsampling-for-large-sample-logistic","title":"Optimal Subsampling for Large Sample Logistic Regression","date":"2017-02-03","arxiv_id":"1702.01166","repositories_listed":0,"syntology":null},{"url":null,"slug":"sharp-convergence-rates-for-forward","title":"Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models","date":"2017-02-03","arxiv_id":"1702.01000","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-and-compact-saliency-score-regression","title":"A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network","date":"2017-02-02","arxiv_id":"1702.00615","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gaussian-process-regression-model-for","title":"A Gaussian Process Regression Model for Distribution Inputs","date":"2017-01-31","arxiv_id":"1701.09055","repositories_listed":0,"syntology":null},{"url":null,"slug":"prototypal-analysis-and-prototypal-regression","title":"Prototypal Analysis and Prototypal Regression","date":"2017-01-31","arxiv_id":"1701.08916","repositories_listed":0,"syntology":null},{"url":null,"slug":"click-through-rate-prediction-for-contextual","title":"Click Through Rate Prediction for Contextual Advertisment Using Linear Regression","date":"2017-01-30","arxiv_id":"1701.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forest-regression-for-manifold-valued","title":"Random Forest regression for manifold-valued responses","date":"2017-01-29","arxiv_id":"1701.08381","repositories_listed":0,"syntology":null},{"url":null,"slug":"subset-selection-for-multiple-linear","title":"Subset Selection for Multiple Linear Regression via Optimization","date":"2017-01-27","arxiv_id":"1701.07920","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-limits-for-linear","title":"Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity","date":"2017-01-26","arxiv_id":"1701.07895","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-convergence-of-sdca-in-statistical","title":"Linear convergence of SDCA in statistical estimation","date":"2017-01-26","arxiv_id":"1701.07808","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-nonlinear-1-step-causal","title":"Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables","date":"2017-01-23","arxiv_id":"1701.06605","repositories_listed":0,"syntology":null},{"url":null,"slug":"patchwork-kriging-for-large-scale-gaussian","title":"Patchwork Kriging for Large-scale Gaussian Process Regression","date":"2017-01-23","arxiv_id":"1701.06655","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-network-learning-via-topological","title":"Bayesian Network Learning via Topological Order","date":"2017-01-20","arxiv_id":"1701.05654","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-selection-algorithm-for-continuous","title":"Parameter Selection Algorithm For Continuous Variables","date":"2017-01-19","arxiv_id":"1701.05593","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-alternative-to-p-values","title":"A Machine Learning Alternative to P-values","date":"2017-01-18","arxiv_id":"1701.04944","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-efficient-hierarchical-online","title":"Highly Efficient Hierarchical Online Nonlinear Regression Using Second Order Methods","date":"2017-01-18","arxiv_id":"1701.05053","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-and-adaptive-linear-regression-in","title":"Efficient and Adaptive Linear Regression in Semi-Supervised Settings","date":"2017-01-17","arxiv_id":"1701.04889","repositories_listed":0,"syntology":null},{"url":null,"slug":"datenqualitat-in-regressionsproblemen","title":"Datenqualität in Regressionsproblemen","date":"2017-01-16","arxiv_id":"1701.04342","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-regression-with-binary","title":"High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition","date":"2017-01-16","arxiv_id":"1701.04455","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularization-sparse-recovery-and-median-of","title":"Regularization, sparse recovery, and median-of-means tournaments","date":"2017-01-15","arxiv_id":"1701.04112","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-algorithms-with-built-in","title":"Symbolic Regression Algorithms with Built-in Linear Regression","date":"2017-01-13","arxiv_id":"1701.03641","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-regression-with-grossly","title":"Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications","date":"2017-01-11","arxiv_id":"1701.02892","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-image-set-classification-using","title":"Efficient Image Set Classification using Linear Regression based Image Reconstruction","date":"2017-01-10","arxiv_id":"1701.02485","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformation-forests","title":"Transformation Forests","date":"2017-01-09","arxiv_id":"1701.02110","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-trajectories-for-high","title":"Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning","date":"2017-01-05","arxiv_id":"1701.01497","repositories_listed":0,"syntology":null},{"url":null,"slug":"overlapping-cover-local-regression-machines","title":"Overlapping Cover Local Regression Machines","date":"2017-01-05","arxiv_id":"1701.01218","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-incremental-regression","title":"Private Incremental Regression","date":"2017-01-04","arxiv_id":"1701.01093","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-model-selection-consistency-and","title":"Bayesian model selection consistency and oracle inequality with intractable marginal likelihood","date":"2017-01-02","arxiv_id":"1701.00311","repositories_listed":0,"syntology":null},{"url":"/paper/convnets-with-smooth-adaptive-activation","slug":"convnets-with-smooth-adaptive-activation","title":"ConvNets with Smooth Adaptive Activation Functions for Regression","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lazily-adapted-constant-kinky-inference-for","title":"Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control","date":"2016-12-31","arxiv_id":"1701.00178","repositories_listed":0,"syntology":null},{"url":null,"slug":"permuted-and-augmented-stick-breaking","title":"Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression","date":"2016-12-30","arxiv_id":"1612.09413","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-descent-method-for-convex-composite","title":"Geometric descent method for convex composite minimization","date":"2016-12-29","arxiv_id":"1612.09034","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-summarization-using-deep-learning-and","title":"Text Summarization using Deep Learning and Ridge Regression","date":"2016-12-26","arxiv_id":"1612.08333","repositories_listed":0,"syntology":null},{"url":null,"slug":"ks_judpil-fire2016detecting-paraphrases-in","title":"KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model","date":"2016-12-24","arxiv_id":"1612.08171","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-classification-of-graph-based-data","title":"Robust Classification of Graph-Based Data","date":"2016-12-21","arxiv_id":"1612.07141","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-approach-to-continuous-self","title":"A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking for Head-Mounted Virtual Reality Systems","date":"2016-12-20","arxiv_id":"1612.06919","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convex-program-for-mixed-linear-regression","title":"A Convex Program for Mixed Linear Regression with a Recovery Guarantee for Well-Separated Data","date":"2016-12-19","arxiv_id":"1612.06067","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-generalized-cross-validation-for","title":"Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality","date":"2016-12-18","arxiv_id":"1612.05907","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-recurrent-neural-network-based","title":"A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study","date":"2016-12-17","arxiv_id":"1612.05794","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-linear-and-bayesian-models","title":"Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems","date":"2016-12-17","arxiv_id":"1612.05740","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-dense-feature-sdm-on-an-iphone","title":"Fast, Dense Feature SDM on an iPhone","date":"2016-12-16","arxiv_id":"1612.05332","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-optimal-control-of-synchronization","title":"Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression","date":"2016-12-15","arxiv_id":"1612.05276","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictability-hidden-by-anomalous","title":"Predictability Hidden by Anomalous Observations","date":"2016-12-15","arxiv_id":"1612.05072","repositories_listed":0,"syntology":null},{"url":null,"slug":"projected-regression-methods-for-inverting","title":"Projected Regression Methods for Inverting Fredholm Integrals: Formalism and Application to Analytical Continuation","date":"2016-12-15","arxiv_id":"1612.04895","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-reinforcement-learning-for-real-time","title":"Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes","date":"2016-12-12","arxiv_id":"1612.03780","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-admm-for-nonconvex","title":"An Empirical Study of ADMM for Nonconvex Problems","date":"2016-12-10","arxiv_id":"1612.03349","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-mixture-of-experts-modeling-using-the","title":"Robust mixture of experts modeling using the skew $t$ distribution","date":"2016-12-09","arxiv_id":"1612.06879","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-mixture-of-experts-modeling-using-the-1","title":"Robust mixture of experts modeling using the $t$ distribution","date":"2016-12-09","arxiv_id":"1701.07429","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-deep-learning-with-spiking-neurons-in","title":"Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity","date":"2016-12-09","arxiv_id":"1612.03214","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-prior-elicitation-of-feature","title":"Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction","date":"2016-12-08","arxiv_id":"1612.02802","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-tree-like-curvilinear-structure","title":"Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm","date":"2016-12-08","arxiv_id":"1612.02631","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-communication-efficient-parallel-method-for","title":"A Communication-Efficient Parallel Method for Group-Lasso","date":"2016-12-07","arxiv_id":"1612.02222","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-functional-regression-approach-to-facial","title":"A Functional Regression approach to Facial Landmark Tracking","date":"2016-12-07","arxiv_id":"1612.02203","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-efficient-regression-for-scalable","title":"Highly Efficient Regression for Scalable Person Re-Identification","date":"2016-12-05","arxiv_id":"1612.01341","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-performance-of-neural-networks","title":"Improving the Performance of Neural Networks in Regression Tasks Using Drawering","date":"2016-12-05","arxiv_id":"1612.01589","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-vector-regression-model-for-bigdata","title":"Support vector regression model for BigData systems","date":"2016-12-05","arxiv_id":"1612.01458","repositories_listed":0,"syntology":null}],"record_sha256":"51e9d68c66b36c1c948effeb9758b4fd101017c23636676aeda9115afd1fee40","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}