{"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/meta-learning/papers/36","list_of":"/task/meta-learning","task":"Meta-Learning","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":36,"pages_in_order":36,"rows_per_page":100,"rows":[3501,3569],"of":3569,"counts":{"archive_papers_tagged":3569,"with_a_code_link":1408,"where_syntology_ran_a_sample":420,"not_listed_spam_title":0,"listed":3569,"listed_where_code_ran":420,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":353,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":353,"listed_every_run_a_failure_of_syntologys_instrument":67,"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/meta-learning","prev":"/task/meta-learning/papers/35","next":null,"papers":[{"url":null,"slug":"auto-meta-automated-gradient-based-meta","title":"Auto-Meta: Automated Gradient Based Meta Learner Search","date":"2018-06-11","arxiv_id":"1806.06927","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-meta-learning","title":"Adversarial Meta-Learning","date":"2018-06-08","arxiv_id":"1806.03316","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-by-the-baldwin-effect","title":"Meta-Learning by the Baldwin Effect","date":"2018-06-06","arxiv_id":"1806.07917","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learner-with-linear-nulling","title":"Meta-Learner with Linear Nulling","date":"2018-06-04","arxiv_id":"1806.01010","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-importance-of-attention-in-meta","title":"On the Importance of Attention in Meta-Learning for Few-Shot Text Classification","date":"2018-06-03","arxiv_id":"1806.00852","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-symbolic-regression-benchmarks","title":"Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach","date":"2018-05-25","arxiv_id":"1805.10365","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-low-resource-neural-machine-1","title":"Meta-Learning for Low-Resource Neural Machine Translation","date":"2018-05-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"task-agnostic-meta-learning-for-few-shot","title":"Task-Agnostic Meta-Learning for Few-shot Learning","date":"2018-05-20","arxiv_id":"1805.07722","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-learning-in-a-hierarchical","title":"Continuous Learning in a Hierarchical Multiscale Neural Network","date":"2018-05-15","arxiv_id":"1805.05758","repositories_listed":0,"syntology":null},{"url":null,"slug":"metatrace-online-step-size-tuning-by-meta","title":"Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control","date":"2018-05-10","arxiv_id":"1805.04514","repositories_listed":0,"syntology":null},{"url":null,"slug":"metabags-bagged-meta-decision-trees-for","title":"MetaBags: Bagged Meta-Decision Trees for Regression","date":"2018-04-17","arxiv_id":"1804.06207","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-a-dynamical-language-model","title":"Meta-Learning a Dynamical Language Model","date":"2018-03-28","arxiv_id":"1803.10631","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-reinforcement-learning-with-latent","title":"Meta Reinforcement Learning with Latent Variable Gaussian Processes","date":"2018-03-20","arxiv_id":"1803.07551","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-theory-of-mind","title":"Machine Theory of Mind","date":"2018-02-21","arxiv_id":"1802.07740","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-tensor-learning-for-large","title":"Multi-resolution Tensor Learning for Large-Scale Spatial Data","date":"2018-02-19","arxiv_id":"1802.06825","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-meta-learning-learning-to-learn-in-the","title":"Deep Meta-Learning: Learning to Learn in the Concept Space","date":"2018-02-10","arxiv_id":"1802.03596","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-convolutional-neural","title":"Interpretable Deep Convolutional Neural Networks via Meta-learning","date":"2018-02-02","arxiv_id":"1802.00560","repositories_listed":0,"syntology":null},{"url":null,"slug":"recasting-gradient-based-meta-learning-as","title":"Recasting Gradient-Based Meta-Learning as Hierarchical Bayes","date":"2018-01-26","arxiv_id":"1801.08930","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-tracker-fast-and-robust-online","title":"Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers","date":"2018-01-09","arxiv_id":"1801.03049","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-autoencoders-a-flexible-meta-learning","title":"Joint autoencoders: a flexible meta-learning framework","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-word-embedding-via-meta-learning","title":"Lifelong Word Embedding via Meta-Learning","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-context-aware-learner","title":"The Context-Aware Learner","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-meta-learning-for-real-time-visual","title":"Deep Meta Learning for Real-Time Target-Aware Visual Tracking","date":"2017-12-26","arxiv_id":"1712.09153","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-well-does-your-sampler-really-work","title":"How well does your sampler really work?","date":"2017-12-16","arxiv_id":"1712.06006","repositories_listed":0,"syntology":null},{"url":null,"slug":"part-1-training-sets-asg-transforms","title":"Part 1: Training Sets & ASG Transforms","date":"2017-12-15","arxiv_id":"1801.05752","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-heuristic-search-algorithm-using-the","title":"A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach","date":"2017-12-08","arxiv_id":"1712.03043","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-learning-perspective-on-cold-start","title":"A Meta-Learning Perspective on Cold-Start Recommendations for Items","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-as-a-meta-learning","title":"Visual Question Answering as a Meta Learning Task","date":"2017-11-22","arxiv_id":"1711.08105","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-extending-neural-networks-with-loss","title":"On Extending Neural Networks with Loss Ensembles for Text Classification","date":"2017-11-14","arxiv_id":"1711.05170","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-and-universality-deep","title":"Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm","date":"2017-10-31","arxiv_id":"1710.11622","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-optimal-meta-learning-of-classification","title":"Rate-optimal Meta Learning of Classification Error","date":"2017-10-31","arxiv_id":"1710.11315","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-autoregressive-density-estimation","title":"Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions","date":"2017-10-27","arxiv_id":"1710.10304","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-via-feature-label-memory","title":"Meta-Learning via Feature-Label Memory Network","date":"2017-10-19","arxiv_id":"1710.07110","repositories_listed":0,"syntology":null},{"url":null,"slug":"supplementary-meta-learning-towards-a-dynamic","title":"Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-qsar-a-large-scale-application-of-meta","title":"Meta-QSAR: a large-scale application of meta-learning to drug design and discovery","date":"2017-09-12","arxiv_id":"1709.03854","repositories_listed":0,"syntology":null},{"url":null,"slug":"alcn-meta-learning-for-contrast-normalization","title":"ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation","date":"2017-08-31","arxiv_id":"1708.09633","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-mcmc-proposals","title":"Meta-Learning MCMC Proposals","date":"2017-08-21","arxiv_id":"1708.06040","repositories_listed":0,"syntology":null},{"url":null,"slug":"labeled-memory-networks-for-online-model","title":"Labeled Memory Networks for Online Model Adaptation","date":"2017-07-05","arxiv_id":"1707.01461","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-meta-critic-networks-for","title":"Learning to Learn: Meta-Critic Networks for Sample Efficient Learning","date":"2017-06-29","arxiv_id":"1706.09529","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-learning-approach-to-one-step-active","title":"A Meta-Learning Approach to One-Step Active Learning","date":"2017-06-26","arxiv_id":"1706.08334","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-framework-for-automated-driving","title":"Meta learning Framework for Automated Driving","date":"2017-06-11","arxiv_id":"1706.04038","repositories_listed":0,"syntology":null},{"url":null,"slug":"remix-automated-exploration-for-interactive","title":"REMIX: Automated Exploration for Interactive Outlier Detection","date":"2017-05-17","arxiv_id":"1705.05986","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-imitation-learning","title":"One-Shot Imitation Learning","date":"2017-03-21","arxiv_id":"1703.07326","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-meta-learning-by-parallel-algorithm","title":"Online Meta-learning by Parallel Algorithm Competition","date":"2017-02-24","arxiv_id":"1702.07490","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimally-naturalistic-artificial","title":"Minimally Naturalistic Artificial Intelligence","date":"2017-01-14","arxiv_id":"1701.03868","repositories_listed":0,"syntology":null},{"url":null,"slug":"set2model-networks-learning-discriminatively","title":"Set2Model Networks: Learning Discriminatively To Learn Generative Models","date":"2016-12-22","arxiv_id":"1612.07697","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-neural-networks","title":"Learning to Learn Neural Networks","date":"2016-10-19","arxiv_id":"1610.06072","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-image-classification-by-boosting-fuzzy","title":"Fast Image Classification by Boosting Fuzzy Classifiers","date":"2016-10-04","arxiv_id":"1610.01068","repositories_listed":0,"syntology":null},{"url":null,"slug":"bending-the-curve-improving-the-roc-curve","title":"Bending the Curve: Improving the ROC Curve Through Error Redistribution","date":"2016-05-21","arxiv_id":"1605.06652","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-within-projective-simulation","title":"Meta-learning within Projective Simulation","date":"2016-02-25","arxiv_id":"1602.08017","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-ensemble-learning-with-confidence","title":"Adaptive Ensemble Learning with Confidence Bounds","date":"2015-12-23","arxiv_id":"1512.07446","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-analysis-of-the-meta-des-framework-for","title":"A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers","date":"2015-09-02","arxiv_id":"1509.00825","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-representations-of-words-and","title":"Distributed Representations of Words and Documents for Discriminating Similar Languages","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-and-empirical-analysis-of-a","title":"Theoretical and Empirical Analysis of a Parallel Boosting Algorithm","date":"2015-08-06","arxiv_id":"1508.01549","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-stacking-for-semi-supervised-sentiment","title":"Semi-Stacking for Semi-supervised Sentiment Classification","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-generalization-for-medical-concept","title":"Stacked Generalization for Medical Concept Extraction from Clinical Notes","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-of-bounds-on-the-bayes","title":"Meta learning of bounds on the Bayes classifier error","date":"2015-04-27","arxiv_id":"1504.07116","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-retrieval-and-classification-using","title":"Image Retrieval And Classification Using Local Feature Vectors","date":"2014-09-02","arxiv_id":"1409.0749","repositories_listed":0,"syntology":null},{"url":null,"slug":"simcompass-using-deep-learning-word","title":"SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recommending-learning-algorithms-and-their","title":"Recommending Learning Algorithms and Their Associated Hyperparameters","date":"2014-07-07","arxiv_id":"1407.1890","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-recurrent-concepts-in-data-streams","title":"Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform","date":"2014-06-24","arxiv_id":"1406.6114","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-easy-to-use-repository-for-comparing-and","title":"An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage","date":"2014-05-28","arxiv_id":"1405.7292","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-feature-subset-selection-algorithm","title":"A Feature Subset Selection Algorithm Automatic Recommendation Method","date":"2014-02-04","arxiv_id":"1402.0570","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-collective-entity-linking-with","title":"Efficient Collective Entity Linking with Stacking","date":"2013-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grammatical-error-correction-using-feature","title":"Grammatical Error Correction Using Feature Selection and Confidence Tuning","date":"2013-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-ensemble","title":"A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics","date":"2013-09-19","arxiv_id":"1309.5047","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-model-for-grammatical-error","title":"A Hybrid Model For Grammatical Error Correction","date":"2013-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-learning-approach-to-grammatical-error","title":"A Meta Learning Approach to Grammatical Error Correction","date":"2012-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-one-class-classifiers-via-meta","title":"Combining One-Class Classifiers via Meta-Learning","date":"2011-12-22","arxiv_id":"1112.5246","repositories_listed":0,"syntology":null}],"record_sha256":"3094f6fd44eb16646d1b1e40c693ddb2a185bbdd72c608421b888da90d81fce7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}