{"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/model-selection/papers/19","list_of":"/task/model-selection","task":"Model Selection","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":19,"pages_in_order":21,"rows_per_page":100,"rows":[1801,1900],"of":2050,"counts":{"archive_papers_tagged":2050,"with_a_code_link":658,"where_syntology_ran_a_sample":141,"not_listed_spam_title":0,"listed":2050,"listed_where_code_ran":141,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":115,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":115,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/model-selection","prev":"/task/model-selection/papers/18","next":"/task/model-selection/papers/20","papers":[{"url":null,"slug":"on-bayesian-exponentially-embedded-family-for","title":"On Bayesian Exponentially Embedded Family for Model Order Selection","date":"2017-03-30","arxiv_id":"1703.10513","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandit-based-model-selection-for-deformable","title":"Bandit-Based Model Selection for Deformable Object Manipulation","date":"2017-03-29","arxiv_id":"1703.10254","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-use-of-default-parameter-settings-in","title":"On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms","date":"2017-03-20","arxiv_id":"1703.06777","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-snr-consistent-compressive-sensing","title":"High SNR Consistent Compressive Sensing","date":"2017-03-10","arxiv_id":"1703.03596","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-dimensionality-selection-for-bayesian","title":"Exact Dimensionality Selection for Bayesian PCA","date":"2017-03-08","arxiv_id":"1703.02834","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-variational-laplace-approach-to","title":"The variational Laplace approach to approximate Bayesian inference","date":"2017-03-02","arxiv_id":"1703.02089","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-non-stationarities-in-high-frequency","title":"Modeling non-stationarities in high-frequency financial time series","date":"2017-02-27","arxiv_id":"1212.0479","repositories_listed":0,"syntology":null},{"url":null,"slug":"objective-bayesian-analysis-for-change-point","title":"Objective Bayesian Analysis for Change Point Problems","date":"2017-02-17","arxiv_id":"1702.05462","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":"luria-delbruck-revisited-the-classic","title":"Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution","date":"2017-01-19","arxiv_id":"1701.05627","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":"online-learning-with-regularized-kernel-for","title":"Online Learning with Regularized Kernel for One-class Classification","date":"2017-01-17","arxiv_id":"1701.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-mri-data-using-deep","title":"Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection","date":"2017-01-16","arxiv_id":"1701.04355","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-statistical-decision-for-gaussian","title":"Optimal statistical decision for Gaussian graphical model selection","date":"2017-01-09","arxiv_id":"1701.02071","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":null,"slug":"network-cross-validation-by-edge-sampling","title":"Network cross-validation by edge sampling","date":"2016-12-14","arxiv_id":"1612.04717","repositories_listed":0,"syntology":null},{"url":null,"slug":"clipper-a-low-latency-online-prediction","title":"Clipper: A Low-Latency Online Prediction Serving System","date":"2016-12-09","arxiv_id":"1612.03079","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-for-automated-model","title":"Bayesian optimization for automated model selection","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-for-efficient-model-selection-for","title":"Boosting for Efficient Model Selection for Syntactic Parsing","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pag2admg-an-algorithm-for-the-complete-causal","title":"PAG2ADMG: An Algorithm for the Complete Causal Enumeration of a Markov Equivalence Class","date":"2016-12-01","arxiv_id":"1612.00099","repositories_listed":0,"syntology":null},{"url":null,"slug":"split-lbi-an-iterative-regularization-path","title":"Split LBI: An Iterative Regularization Path with Structural Sparsity","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-inference-for-pairwise-graphical","title":"Statistical Inference for Pairwise Graphical Models Using Score Matching","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dynamic-attention-controlled-cascaded-shape","slug":"dynamic-attention-controlled-cascaded-shape","title":"Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting","date":"2016-11-16","arxiv_id":"1611.05396","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-framing-through-the-casts-of","title":"Analyzing Framing through the Casts of Characters in the News","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inertial-regularization-and-selection-irs","title":"Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity","date":"2016-10-23","arxiv_id":"1610.07216","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-modeling-in-approximate","title":"Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria","date":"2016-10-20","arxiv_id":"1610.06462","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-parallel-bayesian-model-selection","title":"Robust and Parallel Bayesian Model Selection","date":"2016-10-19","arxiv_id":"1610.06194","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-error-minimization-a-new","title":"Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression","date":"2016-10-18","arxiv_id":"1610.05448","repositories_listed":0,"syntology":null},{"url":null,"slug":"going-off-the-grid-iterative-model-selection","title":"Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion","date":"2016-10-18","arxiv_id":"1610.05400","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-intensity-bursts-using-hawkes","title":"Detection of intensity bursts using Hawkes processes: an application to high frequency financial data","date":"2016-10-17","arxiv_id":"1610.05383","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-distributed-sparse","title":"Communication-efficient Distributed Sparse Linear Discriminant Analysis","date":"2016-10-15","arxiv_id":"1610.04798","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-for-gaussian-process","title":"Model Selection for Gaussian Process Regression by Approximation Set Coding","date":"2016-10-04","arxiv_id":"1610.00907","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-parsimonious-solutions-with-ga","title":"Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases","date":"2016-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"universum-learning-for-multiclass-svm","title":"Universum Learning for Multiclass SVM","date":"2016-09-29","arxiv_id":"1609.09162","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-regression-for-image-binarization","title":"Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background","date":"2016-09-26","arxiv_id":"1609.08078","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-conditional-independence-structure","title":"Learning conditional independence structure for high-dimensional uncorrelated vector processes","date":"2016-09-13","arxiv_id":"1609.03772","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-controlled-sequential-lasso","title":"Feedback-Controlled Sequential Lasso Screening","date":"2016-08-21","arxiv_id":"1608.06010","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-collaborative-imaging-genetics","title":"Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions","date":"2016-08-19","arxiv_id":"1608.07251","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-model-selection-methods-for-mutual","title":"Bayesian Model Selection Methods for Mutual and Symmetric $k$-Nearest Neighbor Classification","date":"2016-08-14","arxiv_id":"1608.04063","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dynamic-hierarchical-models-for","title":"Learning Dynamic Hierarchical Models for Anytime Scene Labeling","date":"2016-08-11","arxiv_id":"1608.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"unitn-end-to-end-discourse-parser-for-conll","title":"UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-kernel-methods-and-model-selection-for","title":"Using Kernel Methods and Model Selection for Prediction of Preterm Birth","date":"2016-07-27","arxiv_id":"1607.07959","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-for-fully-unsupervised","title":"Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection","date":"2016-07-20","arxiv_id":"1607.05822","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixel-based-two-view-deterministic","title":"Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data","date":"2016-07-20","arxiv_id":"1607.05839","repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-bounds-on-active-learning-for-graphical","title":"Lower Bounds on Active Learning for Graphical Model Selection","date":"2016-07-08","arxiv_id":"1607.02413","repositories_listed":0,"syntology":null},{"url":null,"slug":"ordering-as-privileged-information","title":"Ordering as privileged information","date":"2016-06-30","arxiv_id":"1606.09577","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-gaussian-markov-models-for-conditional","title":"A review of Gaussian Markov models for conditional independence","date":"2016-06-23","arxiv_id":"1606.07282","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretability-in-linear-brain-decoding","title":"Interpretability in Linear Brain Decoding","date":"2016-06-17","arxiv_id":"1606.05672","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-interpretability-of-machine","title":"Model-Agnostic Interpretability of Machine Learning","date":"2016-06-16","arxiv_id":"1606.05386","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-variable-graphical-model-selection","title":"Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-consistency-from-the","title":"Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso","date":"2016-06-01","arxiv_id":"1606.00142","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-clustering-and-model-selection-1","title":"Simultaneous Clustering and Model Selection for Tensor Affinities","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-coarse-graining","title":"Predictive Coarse-Graining","date":"2016-05-26","arxiv_id":"1605.08301","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-model-selection-of-stochastic-block","title":"Bayesian Model Selection of Stochastic Block Models","date":"2016-05-23","arxiv_id":"1605.07057","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-nearest-neighbor-learning-in-metric","title":"Active Nearest-Neighbor Learning in Metric Spaces","date":"2016-05-22","arxiv_id":"1605.06792","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-variable-selection-for-globally","title":"Bayesian Variable Selection for Globally Sparse Probabilistic PCA","date":"2016-05-19","arxiv_id":"1605.05918","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-quality-of-the-covariance-selection","title":"The Quality of the Covariance Selection Through Detection Problem and AUC Bounds","date":"2016-05-18","arxiv_id":"1605.05776","repositories_listed":0,"syntology":null},{"url":null,"slug":"combinatorially-generated-piecewise","title":"Combinatorially Generated Piecewise Activation Functions","date":"2016-05-17","arxiv_id":"1605.05216","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-topology-of-large-open-connectome","title":"The topology of large Open Connectome networks for the human brain","date":"2016-05-13","arxiv_id":"1512.01197","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-rates-with-high-probability-in-exp","title":"Fast rates with high probability in exp-concave statistical learning","date":"2016-05-04","arxiv_id":"1605.01288","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-requirements-for-stable","title":"Sampling Requirements for Stable Autoregressive Estimation","date":"2016-05-04","arxiv_id":"1605.01436","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-distributed-estimation-of-inverse","title":"Efficient Distributed Estimation of Inverse Covariance Matrices","date":"2016-05-03","arxiv_id":"1605.00758","repositories_listed":0,"syntology":null},{"url":null,"slug":"markov-models-for-ocular-fixation-locations","title":"Markov models for ocular fixation locations in the presence and absence of colour","date":"2016-04-21","arxiv_id":"1604.06335","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathway-lasso-estimate-and-select-sparse","title":"Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators","date":"2016-03-24","arxiv_id":"1603.07749","repositories_listed":0,"syntology":null},{"url":"/paper/face-recognition-using-deep-multi-pose","slug":"face-recognition-using-deep-multi-pose","title":"Face Recognition Using Deep Multi-Pose Representations","date":"2016-03-23","arxiv_id":"1603.07388","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-between-deep-neural-nets-and","title":"A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition","date":"2016-03-18","arxiv_id":"1603.05800","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-model-selection-in-the-highly-under","title":"Sparse model selection in the highly under-sampled regime","date":"2016-03-03","arxiv_id":"1603.00952","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-cumulative-biological-phenomena-with","title":"Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks","date":"2016-02-25","arxiv_id":"1602.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-model-selection-by-limiting-svm-training","title":"Fast model selection by limiting SVM training times","date":"2016-02-10","arxiv_id":"1602.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tractable-fully-bayesian-method-for-the","title":"A Tractable Fully Bayesian Method for the Stochastic Block Model","date":"2016-02-06","arxiv_id":"1602.02256","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-column-selection-in-approximate-kernel","title":"On Column Selection in Approximate Kernel Canonical Correlation Analysis","date":"2016-02-05","arxiv_id":"1602.02172","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-algorithms-for-graphical","title":"Active Learning Algorithms for Graphical Model Selection","date":"2016-02-01","arxiv_id":"1602.00354","repositories_listed":0,"syntology":null},{"url":"/paper/deep-learning-for-smile-recognition","slug":"deep-learning-for-smile-recognition","title":"Deep Learning For Smile Recognition","date":"2016-01-30","arxiv_id":"1602.00172","repositories_listed":0,"syntology":null},{"url":null,"slug":"cox-process-representation-and-inference-for","title":"Cox process representation and inference for stochastic reaction-diffusion processes","date":"2016-01-08","arxiv_id":"1601.01972","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognito-automated-feature-engineering-for","title":"Cognito: Automated Feature Engineering for Supervised Learning","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"post-regularization-inference-for-time","title":"Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models","date":"2015-12-28","arxiv_id":"1512.08298","repositories_listed":0,"syntology":null},{"url":null,"slug":"kauffmans-adjacent-possible-in-word-order","title":"Kauffman's adjacent possible in word order evolution","date":"2015-12-17","arxiv_id":"1512.05582","repositories_listed":0,"syntology":null},{"url":null,"slug":"blockout-dynamic-model-selection-for","title":"Blockout: Dynamic Model Selection for Hierarchical Deep Networks","date":"2015-12-16","arxiv_id":"1512.05246","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-time-asymptotics-for-the-implied","title":"Short-time asymptotics for the implied volatility skew under a stochastic volatility model with L\\'evy jumps","date":"2015-12-13","arxiv_id":"1502.02595","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-sequential-model-selection","title":"Selective Sequential Model Selection","date":"2015-12-08","arxiv_id":"1512.02565","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-active-model-selection-with-an","title":"Bayesian Active Model Selection with an Application to Automated Audiometry","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-network-models-for-adaptive-testing","title":"Bayesian Network Models for Adaptive Testing","date":"2015-11-26","arxiv_id":"1511.08488","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-arbitrary-view-face-alignment-by","title":"Towards Arbitrary-View Face Alignment by Recommendation Trees","date":"2015-11-20","arxiv_id":"1511.06627","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetrically-weighted-cca-and-hierarchical","title":"Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval","date":"2015-11-19","arxiv_id":"1511.06267","repositories_listed":0,"syntology":null},{"url":null,"slug":"block-diagonal-covariance-selection-for-high","title":"Block-diagonal covariance selection for high-dimensional Gaussian graphical models","date":"2015-11-12","arxiv_id":"1511.04033","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-falling-rule-lists","title":"Causal Falling Rule Lists","date":"2015-10-18","arxiv_id":"1510.05189","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-asymptotics-for-the-parametric","title":"Higher-order asymptotics for the parametric complexity","date":"2015-10-01","arxiv_id":"1510.00112","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-theory-of-deep-learning-via","title":"A Statistical Theory of Deep Learning via Proximal Splitting","date":"2015-09-20","arxiv_id":"1509.06061","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-number-of-autoregressive","title":"Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics","date":"2015-09-11","arxiv_id":"1509.03381","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-online-convex-optimization-by-putting","title":"Deep Online Convex Optimization by Putting Forecaster to Sleep","date":"2015-09-06","arxiv_id":"1509.01851","repositories_listed":0,"syntology":null},{"url":null,"slug":"em-algorithms-for-weighted-data-clustering","title":"EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis","date":"2015-09-04","arxiv_id":"1509.01509","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-neural-networks-for-smile","title":"Deep Convolutional Neural Networks for Smile Recognition","date":"2015-08-26","arxiv_id":"1508.06535","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-online-learning","title":"Adaptive Online Learning","date":"2015-08-21","arxiv_id":"1508.05170","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-out-of-sample-extension-of-graph","title":"Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks","date":"2015-08-18","arxiv_id":"1508.04422","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-aic-and-bic-a-new-criterion-for","title":"Bridging AIC and BIC: a new criterion for autoregression","date":"2015-08-11","arxiv_id":"1508.02473","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structural-kernels-for-natural","title":"Learning Structural Kernels for Natural Language Processing","date":"2015-08-10","arxiv_id":"1508.02131","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-approximation-of-edge-density-in","title":"Universal Approximation of Edge Density in Large Graphs","date":"2015-08-06","arxiv_id":"1508.01340","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-stability-over-noisy-sources","title":"Topic Stability over Noisy Sources","date":"2015-08-05","arxiv_id":"1508.01067","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustness-in-sparse-linear-models-relative","title":"Robustness in sparse linear models: relative efficiency based on robust approximate message passing","date":"2015-07-31","arxiv_id":"1507.08726","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-approximate-bayesian-computation-for","title":"Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations","date":"2015-07-17","arxiv_id":"1507.05117","repositories_listed":0,"syntology":null}],"record_sha256":"892a1304ced0a25683f9b68a00deed1bbde36d2bf7e06ca40b3104877b193e0b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}