{"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/47","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":47,"pages_in_order":95,"rows_per_page":100,"rows":[4601,4700],"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/46","next":"/task/regression-1/papers/48","papers":[{"url":null,"slug":"discrimloss-a-universal-loss-for-hard-samples","title":"DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination","date":"2022-08-21","arxiv_id":"2208.09884","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-descent-in-the-multiple-random","title":"Multiple Descent in the Multiple Random Feature Model","date":"2022-08-21","arxiv_id":"2208.09897","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-regression-analysis-with-pade-approximants","title":"On regression analysis with Padé approximants","date":"2022-08-21","arxiv_id":"2208.09945","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-plug-and-play-approach-for","title":"A Novel Plug-and-Play Approach for Adversarially Robust Generalization","date":"2022-08-19","arxiv_id":"2208.09449","repositories_listed":0,"syntology":null},{"url":null,"slug":"carefully-choose-the-baseline-lessons-learned","title":"Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience","date":"2022-08-19","arxiv_id":"2208.09473","repositories_listed":0,"syntology":null},{"url":null,"slug":"game-theoretic-algorithms-for-conditional","title":"Game-Theoretic Algorithms for Conditional Moment Matching","date":"2022-08-19","arxiv_id":"2208.09551","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-high-dimensional-ising","title":"Meta Learning for High-dimensional Ising Model Selection Using $\\ell_1$-regularized Logistic Regression","date":"2022-08-19","arxiv_id":"2208.09539","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-optimization-for-unsupervised-1","title":"Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm","date":"2022-08-19","arxiv_id":"2208.09454","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiased-inference-on-identified-linear","title":"Inference on Strongly Identified Functionals of Weakly Identified Functions","date":"2022-08-17","arxiv_id":"2208.08291","repositories_listed":0,"syntology":null},{"url":null,"slug":"look-in-different-views-multi-scheme","title":"Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation","date":"2022-08-17","arxiv_id":"2208.08078","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-neural-network-representation-of","title":"Shallow neural network representation of polynomials","date":"2022-08-17","arxiv_id":"2208.08138","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-parity-fairness-testing-for-group","title":"Error Parity Fairness: Testing for Group Fairness in Regression Tasks","date":"2022-08-16","arxiv_id":"2208.08279","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-seismic-intensity-distributions","title":"Prediction of Seismic Intensity Distributions Using Neural Networks","date":"2022-08-16","arxiv_id":"2208.07565","repositories_listed":0,"syntology":null},{"url":null,"slug":"acceleration-of-subspace-learning-machine-via","title":"Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing","date":"2022-08-15","arxiv_id":"2208.07023","repositories_listed":0,"syntology":null},{"url":null,"slug":"easy-differentially-private-linear-regression","title":"Easy Differentially Private Linear Regression","date":"2022-08-15","arxiv_id":"2208.07353","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-networks-for-predictive-group","title":"Transformer Networks for Predictive Group Elevator Control","date":"2022-08-15","arxiv_id":"2208.08948","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssp-pose-symmetry-aware-shape-prior","title":"SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation","date":"2022-08-13","arxiv_id":"2208.06661","repositories_listed":0,"syntology":null},{"url":null,"slug":"interaction-decompositions-for-tensor-network","title":"Interaction Decompositions for Tensor Network Regression","date":"2022-08-11","arxiv_id":"2208.06029","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-in-front-of-statistical","title":"Machine learning in front of statistical methods for prediction spread SARS-CoV-2 in Colombia","date":"2022-08-11","arxiv_id":"2208.05910","repositories_listed":0,"syntology":null},{"url":null,"slug":"copulaboost-additive-modeling-with-copula","title":"Boosting with copula-based components","date":"2022-08-09","arxiv_id":"2208.04669","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-polynomial-neural-ordinary","title":"Interpretable Polynomial Neural Ordinary Differential Equations","date":"2022-08-09","arxiv_id":"2208.05072","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-least-square-regression-via-three","title":"Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding","date":"2022-08-09","arxiv_id":"2208.04324","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-properties-of-the-log-cosh-loss","title":"Statistical Properties of the log-cosh Loss Function Used in Machine Learning","date":"2022-08-09","arxiv_id":"2208.04564","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-bottleneck-theory-of-high","title":"Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality","date":"2022-08-08","arxiv_id":"2208.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-to-predict-performance","title":"Learning to Learn to Predict Performance Regressions in Production at Meta","date":"2022-08-08","arxiv_id":"2208.04351","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-heterogeneous-treatment-effects","title":"Quantile Random-Coefficient Regression with Interactive Fixed Effects: Heterogeneous Group-Level Policy Evaluation","date":"2022-08-07","arxiv_id":"2208.03632","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-point-estimation-for-the-rayleigh","title":"Improved Point Estimation for the Rayleigh Regression Model","date":"2022-08-07","arxiv_id":"2208.03611","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-balanced-distillation-for-object","title":"Task-Balanced Distillation for Object Detection","date":"2022-08-05","arxiv_id":"2208.03006","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-with-informative","title":"Bayesian Optimization with Informative Covariance","date":"2022-08-04","arxiv_id":"2208.02704","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-deep-learning-for-directly","title":"End-to-end deep learning for directly estimating grape yield from ground-based imagery","date":"2022-08-04","arxiv_id":"2208.02394","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-signal-processing-for-heterogeneous","title":"Graph Signal Processing for Heterogeneous Change Detection Part II: Spectral Domain Analysis","date":"2022-08-03","arxiv_id":"2208.01905","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-and-limitation-of-pretraining","title":"The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift","date":"2022-08-03","arxiv_id":"2208.01857","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubly-robust-estimation-of-local-average","title":"Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment","date":"2022-08-02","arxiv_id":"2208.01300","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-penalized-two-pass-regression-to-predict","title":"A penalized two-pass regression to predict stock returns with time-varying risk premia","date":"2022-08-01","arxiv_id":"2208.00972","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evidential-neural-network-model-for","title":"An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers","date":"2022-08-01","arxiv_id":"2208.00647","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-visual-inertial-deep-multimodal","title":"Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression","date":"2022-08-01","arxiv_id":"2208.00919","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-omitted-variables-on-the-sign","title":"The Effect of Omitted Variables on the Sign of Regression Coefficients","date":"2022-08-01","arxiv_id":"2208.00552","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-rayleigh-regression-method-for-sar","title":"Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers","date":"2022-07-29","arxiv_id":"2208.00097","repositories_listed":0,"syntology":null},{"url":null,"slug":"hob-cnn-hallucination-of-occluded-branches","title":"HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees","date":"2022-07-28","arxiv_id":"2208.00002","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-with-gini-indices-under-auto","title":"Model selection with Gini indices under auto-calibration","date":"2022-07-28","arxiv_id":"2207.14372","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-design-of-quadratic-neural","title":"Analysis and Design of Quadratic Neural Networks for Regression, Classification, and Lyapunov Control of Dynamical Systems","date":"2022-07-26","arxiv_id":"2207.13120","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-estimation-via","title":"Differentially Private Estimation via Statistical Depth","date":"2022-07-26","arxiv_id":"2207.12602","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-to-predict-the-antimicrobial","title":"Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids","date":"2022-07-25","arxiv_id":"2207.12478","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-bayesian-state-space-and-time-varying","title":"Sparse Bayesian State-Space and Time-Varying Parameter Models","date":"2022-07-25","arxiv_id":"2207.12147","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-analysis-for-automated","title":"Spatial-temporal Analysis for Automated Concrete Workability Estimation","date":"2022-07-24","arxiv_id":"2207.11635","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-non-crossing-quantile","title":"Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks","date":"2022-07-21","arxiv_id":"2207.10442","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-financial-networks-using-quantile","title":"Exploring Financial Networks Using Quantile Regression and Granger Causality","date":"2022-07-21","arxiv_id":"2207.10705","repositories_listed":0,"syntology":null},{"url":null,"slug":"correntropy-based-logistic-regression-with","title":"Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding","date":"2022-07-20","arxiv_id":"2207.09693","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-tuning-the-elasticnet-across","title":"Provably tuning the ElasticNet across instances","date":"2022-07-20","arxiv_id":"2207.10199","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-testing-for-connected-and-automated","title":"Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios","date":"2022-07-19","arxiv_id":"2207.09259","repositories_listed":0,"syntology":null},{"url":"/paper/an-efficient-method-for-face-quality","slug":"an-efficient-method-for-face-quality","title":"An Efficient Method for Face Quality Assessment on the Edge","date":"2022-07-19","arxiv_id":"2207.09505","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-endogeneity-correction-based-on-a","title":"Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations","date":"2022-07-19","arxiv_id":"2207.09246","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-vertical-logistic-regression-privacy","title":"Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond","date":"2022-07-19","arxiv_id":"2207.09087","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-deep-belief-network-based-auto","title":"Explainable Deep Belief Network based Auto encoder using novel Extended Garson Algorithm","date":"2022-07-18","arxiv_id":"2207.08501","repositories_listed":0,"syntology":null},{"url":null,"slug":"implementation-of-machine-learning-based-der","title":"Implementation of Machine Learning-based DER Local Control Schemes on Measurement Devices for Counteracting Communication Failures","date":"2022-07-18","arxiv_id":"2207.08732","repositories_listed":0,"syntology":null},{"url":null,"slug":"isotonic-propensity-score-matching","title":"Isotonic propensity score matching","date":"2022-07-18","arxiv_id":"2207.08868","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-regression-with-modified-relu","title":"Nonparametric regression with modified ReLU networks","date":"2022-07-17","arxiv_id":"2207.08306","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-lewis-weight-sampling","title":"Online Lewis Weight Sampling","date":"2022-07-17","arxiv_id":"2207.08268","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-calibration-in-bayesian-neural","title":"Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors","date":"2022-07-17","arxiv_id":"2207.08200","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-sketches-for-robust-regression-with","title":"Adaptive Sketches for Robust Regression with Importance Sampling","date":"2022-07-16","arxiv_id":"2207.07822","repositories_listed":0,"syntology":null},{"url":null,"slug":"work-in-progress-safety-and-robustness","title":"Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool","date":"2022-07-14","arxiv_id":"2207.06759","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-merging-feature-engineering-and-deep","title":"On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG","date":"2022-07-13","arxiv_id":"2207.06096","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-active-regression","title":"Online Active Regression","date":"2022-07-13","arxiv_id":"2207.05945","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-differences-in-differences","title":"Two-stage differences in differences","date":"2022-07-13","arxiv_id":"2207.05943","repositories_listed":0,"syntology":null},{"url":null,"slug":"agboost-attention-based-modification-of","title":"AGBoost: Attention-based Modification of Gradient Boosting Machine","date":"2022-07-12","arxiv_id":"2207.05724","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-energy-based-models-for-implicit","title":"Conditional Energy-Based Models for Implicit Policies: The Gap between Theory and Practice","date":"2022-07-12","arxiv_id":"2207.05824","repositories_listed":0,"syntology":null},{"url":null,"slug":"coronavirus-disease-situation-analysis-and","title":"Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population","date":"2022-07-12","arxiv_id":"2207.13056","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-waste-copper-granules-rating-system-based","title":"A Waste Copper Granules Rating System Based on Machine Vision","date":"2022-07-11","arxiv_id":"2207.04575","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearly-optimal-private-linear-regression-via","title":"(Nearly) Optimal Private Linear Regression via Adaptive Clipping","date":"2022-07-11","arxiv_id":"2207.04686","repositories_listed":0,"syntology":null},{"url":null,"slug":"rrmse-voting-regressor-a-weighting-function","title":"RRMSE Voting Regressor: A weighting function based improvement to ensemble regression","date":"2022-07-11","arxiv_id":"2207.04837","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-trees-regression-and-classification","title":"Energy Trees: Regression and Classification With Structured and Mixed-Type Covariates","date":"2022-07-10","arxiv_id":"2207.04430","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-and-self-attention-in-random","title":"Attention and Self-Attention in Random Forests","date":"2022-07-09","arxiv_id":"2207.04293","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-intraday-false-data","title":"Identification of Intraday False Data Injection Attack on DER Dispatch Signals","date":"2022-07-08","arxiv_id":"2207.03667","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-subspace-structure-of-gradient-based","title":"On the Subspace Structure of Gradient-Based Meta-Learning","date":"2022-07-08","arxiv_id":"2207.03804","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-econometrics-for-misaligned-data","title":"Spatial Econometrics for Misaligned Data","date":"2022-07-08","arxiv_id":"2207.04082","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-solver-gradient-descent-training-algorithm","title":"A Solver + Gradient Descent Training Algorithm for Deep Neural Networks","date":"2022-07-07","arxiv_id":"2207.03264","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-from-pre-images-to-learn-heuristic","title":"Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning","date":"2022-07-07","arxiv_id":"2207.03336","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-deep-learning-for-nonparametric-time","title":"Adaptive deep learning for nonlinear time series models","date":"2022-07-06","arxiv_id":"2207.02546","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-conformalized-quantile-regression","title":"Improved conformalized quantile regression","date":"2022-07-06","arxiv_id":"2207.02808","repositories_listed":0,"syntology":null},{"url":null,"slug":"ordinal-regression-via-binary-preference-vs","title":"Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives","date":"2022-07-06","arxiv_id":"2207.02454","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantitative-assessment-of-desis","title":"Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia","date":"2022-07-06","arxiv_id":"2207.02482","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approximation-method-for-fitted-random","title":"An Approximation Method for Fitted Random Forests","date":"2022-07-05","arxiv_id":"2207.02184","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-short-term-impact-of-congestion-taxes-on","title":"The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago","date":"2022-07-05","arxiv_id":"2207.01793","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-mechanisms-for-physiological-signal","title":"Attention mechanisms for physiological signal deep learning: which attention should we take?","date":"2022-07-04","arxiv_id":"2207.06904","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-time-series","title":"Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction","date":"2022-07-03","arxiv_id":"2207.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-is-np-hard","title":"Symbolic Regression is NP-hard","date":"2022-07-03","arxiv_id":"2207.01018","repositories_listed":0,"syntology":null},{"url":null,"slug":"amrita-cen-at-semeval-2022-task-4","title":"Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amrita-cen-at-semeval-2022-task-6-a-machine","title":"Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionally-informed-models-for-detecting","title":"Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-correlation-loss-for-regression","title":"Fine-grained Correlation Loss for Regression","date":"2022-07-01","arxiv_id":"2207.00347","repositories_listed":0,"syntology":null},{"url":null,"slug":"grapheme-to-phoneme-conversion-for-thai-using","title":"Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"huaams-at-semeval-2022-task-8-combining","title":"HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hw-tsc-at-semeval-2022-task-7-ensemble-model","title":"HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kamikla-at-semeval-2022-task-3-alberto-bert","title":"KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT—Be(r)tween Taxonomy Detection and Prediction","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"niksss-at-semeval-2022-task7-transformers-for","title":"niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tcu-at-semeval-2022-task-8-a-stacking","title":"TCU at SemEval-2022 Task 8: A Stacking Ensemble Transformer Model for Multilingual News Article Similarity","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uor-ncl-at-semeval-2022-task-3-fine-tuning","title":"UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mkiou-loss-towards-accurate-oriented-object","title":"MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images","date":"2022-06-30","arxiv_id":"2206.15109","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-machine-learning-for-1","title":"Physics-informed machine learning for Structural Health Monitoring","date":"2022-06-30","arxiv_id":"2206.15303","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-physical-effects-for-effective","title":"Understanding Physical Effects for Effective Tool-use","date":"2022-06-30","arxiv_id":"2206.14998","repositories_listed":0,"syntology":null}],"record_sha256":"6edef69c926cee2803236d4cdd4a4cc215d775b2f4f836c5c28e15a0e532a5bd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}