{"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/82","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":82,"pages_in_order":95,"rows_per_page":100,"rows":[8101,8200],"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/81","next":"/task/regression-1/papers/83","papers":[{"url":null,"slug":"dmgroup-at-emoint-2017-emotion-intensity","title":"DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-enriched-character-level-convolutions","title":"Feature-Enriched Character-Level Convolutions for Text Regression","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-patterns-in-noisy-crowds-regression","title":"Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-consumer-spending-from-purchase","title":"Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nuig-at-emoint-2017-bilstm-and-svr-ensemble","title":"NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pln-pucrs-at-emoint-2017-psycholinguistic","title":"PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-inference-for-data-adaptive","title":"Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes","date":"2017-09-01","arxiv_id":"1709.00401","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-gaze-to-predict-text-readability","title":"Using Gaze to Predict Text Readability","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uwat-emote-at-emoint-2017-emotion-intensity","title":"UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zipporah-a-fast-and-scalable-data-cleaning","title":"Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-class-specific-regression-for-face","title":"Neural Class-Specific Regression for face verification","date":"2017-08-31","arxiv_id":"1708.09642","repositories_listed":0,"syntology":null},{"url":null,"slug":"slope-stability-analysis-with-geometric","title":"Slope Stability Analysis with Geometric Semantic Genetic Programming","date":"2017-08-30","arxiv_id":"1708.09116","repositories_listed":0,"syntology":null},{"url":null,"slug":"measurement-of-common-risk-factors-a-panel","title":"Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns","date":"2017-08-29","arxiv_id":"1708.08622","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compromise-principle-in-deep-monocular","title":"A Compromise Principle in Deep Monocular Depth Estimation","date":"2017-08-28","arxiv_id":"1708.08267","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-sheep-pox-disease-from-the-1994-1998","title":"Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece","date":"2017-08-28","arxiv_id":"1709.01143","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-regression-for","title":"Differentially Private Regression for Discrete-Time Survival Analysis","date":"2017-08-24","arxiv_id":"1708.07436","repositories_listed":0,"syntology":null},{"url":null,"slug":"logistic-regression-as-soft-perceptron","title":"Logistic Regression as Soft Perceptron Learning","date":"2017-08-24","arxiv_id":"1708.07826","repositories_listed":0,"syntology":null},{"url":null,"slug":"causally-regularized-learning-with-agnostic","title":"Causally Regularized Learning with Agnostic Data Selection Bias","date":"2017-08-22","arxiv_id":"1708.06656","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-combinations-of-sigmoids-through","title":"Learning Combinations of Sigmoids Through Gradient Estimation","date":"2017-08-22","arxiv_id":"1708.06678","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-regression-using-deep-neural","title":"Nonparametric regression using deep neural networks with ReLU activation function","date":"2017-08-22","arxiv_id":"1708.06633","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-vs-diverse-architectures-for","title":"Deep vs. Diverse Architectures for Classification Problems","date":"2017-08-21","arxiv_id":"1708.06347","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-primal-dual-proximal-extragradient","title":"Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization","date":"2017-08-20","arxiv_id":"1708.05978","repositories_listed":0,"syntology":null},{"url":null,"slug":"teaching-uavs-to-race-end-to-end-regression","title":"Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation","date":"2017-08-19","arxiv_id":"1708.05884","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-pose-regression-using-convolutional-neural","title":"3D Pose Regression using Convolutional Neural Networks","date":"2017-08-18","arxiv_id":"1708.05628","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-stochastic-replica-approach-to-machine","title":"The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization","date":"2017-08-18","arxiv_id":"1708.05715","repositories_listed":0,"syntology":null},{"url":null,"slug":"extensions-of-morse-smale-regression-with","title":"Extensions of Morse-Smale Regression with Application to Actuarial Science","date":"2017-08-17","arxiv_id":"1708.05712","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequentist-coverage-and-sup-norm-convergence","title":"Frequentist coverage and sup-norm convergence rate in Gaussian process regression","date":"2017-08-16","arxiv_id":"1708.04753","repositories_listed":0,"syntology":null},{"url":null,"slug":"globenet-convolutional-neural-networks-for","title":"GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery","date":"2017-08-11","arxiv_id":"1708.03417","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-polynomial-regression-up-to-the","title":"Robust polynomial regression up to the information theoretic limit","date":"2017-08-10","arxiv_id":"1708.03257","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-is-the-first-spurious-variable-selected","title":"When Is the First Spurious Variable Selected by Sequential Regression Procedures?","date":"2017-08-10","arxiv_id":"1708.03046","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-theory-of-distributed-regression","title":"Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network","date":"2017-08-07","arxiv_id":"1708.01960","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonconvex-sparse-logistic-regression-with","title":"Nonconvex Sparse Logistic Regression with Weakly Convex Regularization","date":"2017-08-07","arxiv_id":"1708.02059","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-low-dimensional-regression-via","title":"Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing","date":"2017-08-06","arxiv_id":"1708.01947","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-noteheads-in-handwritten-scores","title":"Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression","date":"2017-08-05","arxiv_id":"1708.01806","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-guided-regression-network-with-context","title":"Query-guided Regression Network with Context Policy for Phrase Grounding","date":"2017-08-04","arxiv_id":"1708.01676","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-model-selection-via-integral-terms","title":"Sparse model selection via integral terms","date":"2017-08-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-kernel-regression-with-provably","title":"Streaming kernel regression with provably adaptive mean, variance, and regularization","date":"2017-08-02","arxiv_id":"1708.00768","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-heteroscedastic-regression","title":"Active Heteroscedastic Regression","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-support-vector-machine","title":"Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification","date":"2017-08-01","arxiv_id":"1708.00122","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosted-fitted-q-iteration","title":"Boosted Fitted Q-Iteration","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-curse-of-dimensionality-in","title":"Breaking the curse of dimensionality in regression","date":"2017-08-01","arxiv_id":"1708.00430","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-biomedical-information","title":"Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dfki-dkt-at-semeval-2017-task-8-rumour","title":"DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"duth-at-semeval-2017-task-5-sentiment","title":"DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-5-an-ensemble-of","title":"ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"forest-type-regression-with-general-losses","title":"Forest-type Regression with General Losses and Robust Forest","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-structured-quantile","title":"High-Dimensional Structured Quantile Regression","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iba-sys-at-semeval-2017-task-5-fine-grained","title":"IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iitpb-at-semeval-2017-task-5-sentiment","title":"IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"itnlp-aikf-at-semeval-2017-task-1-rich","title":"ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"macquarie-university-at-bioasq-5b-a-query","title":"Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitre-at-semeval-2017-task-1-simple-semantic","title":"MITRE at SemEval-2017 Task 1: Simple Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-context-words-as-regions-an-ordinal","title":"Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-rating-regression-with-abstractive","title":"Neural Rating Regression with Abstractive Tips Generation for Recommendation","date":"2017-08-01","arxiv_id":"1708.00154","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-under-uncertainty-in-sparse","title":"Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"purduenlp-at-semeval-2017-task-1-predicting","title":"PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rule-enhanced-penalized-regression-by-column","title":"Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"source-target-similarity-modelings-for-multi","title":"Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sts-uhh-at-semeval-2017-task-1-scoring","title":"STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"takelab-at-semeval-2017-task-5-linear","title":"TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twise-at-semeval-2017-task-4-five-point","title":"TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-by-robust-statistics","title":"Anomaly Detection by Robust Statistics","date":"2017-07-31","arxiv_id":"1707.09752","repositories_listed":0,"syntology":null},{"url":null,"slug":"knn-ensembles-for-tweedie-regression-the","title":"KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods","date":"2017-07-29","arxiv_id":"1708.02122","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-algorithms-for-non-convex-isotonic","title":"Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization","date":"2017-07-28","arxiv_id":"1707.09157","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-regression-networks","title":"Tensor Regression Networks","date":"2017-07-26","arxiv_id":"1707.08308","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-bounds-for-piecewise-smooth-and","title":"Error Bounds for Piecewise Smooth and Switching Regression","date":"2017-07-25","arxiv_id":"1707.07938","repositories_listed":0,"syntology":null},{"url":null,"slug":"restricted-eigenvalue-from-stable-rank-with","title":"Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression","date":"2017-07-25","arxiv_id":"1707.08092","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-approximate-bayesian-computation","title":"Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions","date":"2017-07-24","arxiv_id":"1707.07498","repositories_listed":0,"syntology":null},{"url":null,"slug":"big-data-regression-using-tree-based","title":"Big Data Regression Using Tree Based Segmentation","date":"2017-07-24","arxiv_id":"1707.07409","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-gradient-descent-for-relational","title":"Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls","date":"2017-07-24","arxiv_id":"1707.07716","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-civilians-killed-by-police-with","title":"Identifying civilians killed by police with distantly supervised entity-event extraction","date":"2017-07-22","arxiv_id":"1707.07086","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-gender-of-indonesian-names","title":"Predicting the Gender of Indonesian Names","date":"2017-07-22","arxiv_id":"1707.07129","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-asymptotic-uniform-rates-of-consistency","title":"Non-Asymptotic Uniform Rates of Consistency for k-NN Regression","date":"2017-07-19","arxiv_id":"1707.06261","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-optimization-for-low-dimensional","title":"Global optimization for low-dimensional switching linear regression and bounded-error estimation","date":"2017-07-18","arxiv_id":"1707.05533","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-gaussian-process-regression","title":"Latent Gaussian Process Regression","date":"2017-07-18","arxiv_id":"1707.05534","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-filter-tracking","title":"Spectral Filter Tracking","date":"2017-07-18","arxiv_id":"1707.05553","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-naive-bayes-for-regression-with","title":"Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data","date":"2017-07-16","arxiv_id":"1707.04943","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-liability-models-and","title":"Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data","date":"2017-07-14","arxiv_id":"1707.04639","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cluster-elastic-net-for-multivariate","title":"A Cluster Elastic Net for Multivariate Regression","date":"2017-07-12","arxiv_id":"1707.03530","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-variable-fidelity-surrogate","title":"Large Scale Variable Fidelity Surrogate Modeling","date":"2017-07-12","arxiv_id":"1707.03916","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-target-inference-models-for-spatial","title":"Source-Target Inference Models for Spatial Instruction Understanding","date":"2017-07-12","arxiv_id":"1707.03804","repositories_listed":0,"syntology":null},{"url":null,"slug":"exhaustive-search-for-sparse-variable","title":"Exhaustive search for sparse variable selection in linear regression","date":"2017-07-07","arxiv_id":"1707.02050","repositories_listed":0,"syntology":null},{"url":null,"slug":"tasselnet-counting-maize-tassels-in-the-wild","title":"TasselNet: Counting maize tassels in the wild via local counts regression network","date":"2017-07-07","arxiv_id":"1707.02290","repositories_listed":0,"syntology":null},{"url":null,"slug":"indefinite-kernel-logistic-regression","title":"Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure","date":"2017-07-06","arxiv_id":"1707.01826","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-system-for-news-articles-using","title":"Recommender System for News Articles using Supervised Learning","date":"2017-07-03","arxiv_id":"1707.00506","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-phalanxes","title":"Regression Phalanxes","date":"2017-07-03","arxiv_id":"1707.00727","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mixture-of-linear-inverse-regressions","title":"Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/lcr-net-localization-classification","slug":"lcr-net-localization-classification","title":"LCR-Net: Localization-Classification-Regression for Human Pose","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistic-analysis-of-differences-in","title":"Linguistic analysis of differences in portrayal of movie characters","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-sparse-subspace-representation-for","title":"Low-Rank-Sparse Subspace Representation for Robust Regression","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-tracking-with-quadruplet","title":"Multi-Object Tracking With Quadruplet Convolutional Neural Networks","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-nonlinear-mixed-effects-models","title":"Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-margin-mixture-of-regressions","title":"Soft-Margin Mixture of Regressions","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-clustering-via-variance-regularized","title":"Subspace Clustering via Variance Regularized Ridge Regression","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"surface-motion-capture-transfer-with-gaussian","title":"Surface Motion Capture Transfer With Gaussian Process Regression","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-orientation-of-tweets-for-predicting","title":"Temporal Orientation of Tweets for Predicting Income of Users","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotic-confidence-regions-for-high","title":"Asymptotic Confidence Regions for High-dimensional Structured Sparsity","date":"2017-06-28","arxiv_id":"1706.09231","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-steepest-coordinate-descent","title":"Approximate Steepest Coordinate Descent","date":"2017-06-26","arxiv_id":"1706.08427","repositories_listed":0,"syntology":null},{"url":null,"slug":"yotube-searching-action-proposal-via","title":"YoTube: Searching Action Proposal via Recurrent and Static Regression Networks","date":"2017-06-26","arxiv_id":"1706.08218","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-abstraction-for-multi-scale","title":"Statistical abstraction for multi-scale spatio-temporal systems","date":"2017-06-22","arxiv_id":"1706.07005","repositories_listed":0,"syntology":null}],"record_sha256":"910d15ba9c05bcb8ba63940d9fb7fa2b0ab5bd2972cd695e9a4c07cfd21925d2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}