{"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":"/method/early-stopping/papers/5","list_of":"/method/early-stopping","method":"Early Stopping","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":5,"pages_in_order":5,"rows_per_page":100,"rows":[401,468],"of":468,"counts":{"archive_papers_tagged":468,"with_a_code_link":197,"where_syntology_ran_a_sample":51,"not_listed_spam_title":0,"listed":468,"listed_where_code_ran":51,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":41,"every_run_a_failure_of_syntologys_instrument":10,"listed_with_a_run_with_no_instrument_failure":41,"listed_every_run_a_failure_of_syntologys_instrument":10,"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":"/method/early-stopping","prev":"/method/early-stopping/papers/4","next":null,"papers":[{"paper":null,"slug":"improved-consistency-regularization-for-gans","title":"Improved Consistency Regularization for GANs","date":"2020-02-11","arxiv_id":"2002.04724","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconstructing-natural-scenes-from-fmri","title":"Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN","date":"2020-01-31","arxiv_id":"2001.11761","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-optimization-of-plain","slug":"stochastic-optimization-of-plain","title":"Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods","date":"2020-01-24","arxiv_id":"2001.08856","n_code_links":1,"syntology":null},{"paper":null,"slug":"generate-high-resolution-adversarial-samples","title":"HRFA: High-Resolution Feature-based Attack","date":"2020-01-21","arxiv_id":"2001.07631","n_code_links":0,"syntology":null},{"paper":null,"slug":"random-matrix-theory-proves-that-deep-1","title":"Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures","date":"2020-01-21","arxiv_id":"2001.08370","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-stopping-rule-for-kernel-based","title":"Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms","date":"2020-01-09","arxiv_id":"2001.02879","n_code_links":0,"syntology":null},{"paper":"/paper/cnn-generated-images-are-surprisingly-easy-to","slug":"cnn-generated-images-are-surprisingly-easy-to","title":"CNN-generated images are surprisingly easy to spot... for now","date":"2019-12-23","arxiv_id":"1912.11035","n_code_links":5,"syntology":null},{"paper":"/paper/the-spectral-bias-of-the-deep-image-prior","slug":"the-spectral-bias-of-the-deep-image-prior","title":"The Spectral Bias of the Deep Image Prior","date":"2019-12-18","arxiv_id":"1912.08905","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PCJohn/dip-spectral"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"detecting-gan-generated-errors","title":"Detecting GAN generated errors","date":"2019-12-02","arxiv_id":"1912.00527","n_code_links":0,"syntology":null},{"paper":"/paper/logan-latent-optimisation-for-generative-1","slug":"logan-latent-optimisation-for-generative-1","title":"LOGAN: Latent Optimisation for Generative Adversarial Networks","date":"2019-12-02","arxiv_id":"1912.00953","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/semantic-hierarchy-emerges-in-deep-generative","slug":"semantic-hierarchy-emerges-in-deep-generative","title":"Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis","date":"2019-11-21","arxiv_id":"1911.09267","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":2,"n_instrument":2,"unverified":1,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/improving-singing-voice-separation-with-the","slug":"improving-singing-voice-separation-with-the","title":"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy","date":"2019-10-22","arxiv_id":"1910.10071","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-inductive-bias-of-neural-networks","slug":"leveraging-inductive-bias-of-neural-networks","title":"Image recognition from raw labels collected without annotators","date":"2019-10-20","arxiv_id":"1910.09055","n_code_links":1,"syntology":null},{"paper":"/paper/improving-sample-diversity-of-a-pre-trained","slug":"improving-sample-diversity-of-a-pre-trained","title":"A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings","date":"2019-10-10","arxiv_id":"1910.04760","n_code_links":1,"syntology":null},{"paper":"/paper/distillation-approx-early-stopping-harvesting","slug":"distillation-approx-early-stopping-harvesting","title":"Distillation $\\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network","date":"2019-10-02","arxiv_id":"1910.01255","n_code_links":1,"syntology":null},{"paper":"/paper/drawing-early-bird-tickets-towards-more","slug":"drawing-early-bird-tickets-towards-more","title":"Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks","date":"2019-09-26","arxiv_id":"1909.11957","n_code_links":2,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["RICE-EIC/Early-Bird-Tickets"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/ir-nas-neural-architecture-search-for-image","slug":"ir-nas-neural-architecture-search-for-image","title":"Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising","date":"2019-09-18","arxiv_id":"1909.08228","n_code_links":1,"syntology":null},{"paper":null,"slug":"k-relevance-vectors-for-pattern","title":"k-Relevance Vectors: Considering Relevancy Beside Nearness","date":"2019-09-18","arxiv_id":"1909.08528","n_code_links":0,"syntology":null},{"paper":null,"slug":"darts-improved-differentiable-architecture","title":"DARTS+: Improved Differentiable Architecture Search with Early Stopping","date":"2019-09-13","arxiv_id":"1909.06035","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-realistic-practices-in-low-resource","title":"Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set","date":"2019-09-04","arxiv_id":"1909.01522","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-video-generation-on-complex","slug":"efficient-video-generation-on-complex","title":"Adversarial Video Generation on Complex Datasets","date":"2019-07-15","arxiv_id":"1907.06571","n_code_links":1,"syntology":null},{"paper":"/paper/large-scale-adversarial-representation","slug":"large-scale-adversarial-representation","title":"Large Scale Adversarial Representation Learning","date":"2019-07-04","arxiv_id":"1907.02544","n_code_links":4,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"generalization-guarantees-for-neural-networks","title":"Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian","date":"2019-06-12","arxiv_id":"1906.05392","n_code_links":0,"syntology":null},{"paper":null,"slug":"off-policy-evaluation-via-off-policy","title":"Off-Policy Evaluation via Off-Policy Classification","date":"2019-06-04","arxiv_id":"1906.01624","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-theory-behind-overfitting-cross","title":"The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial","date":"2019-05-28","arxiv_id":"1905.12787","n_code_links":0,"syntology":null},{"paper":null,"slug":"style-transfer-based-image-synthesis-as-an","title":"Style transfer-based image synthesis as an efficient regularization technique in deep learning","date":"2019-05-27","arxiv_id":"1905.10974","n_code_links":0,"syntology":null},{"paper":null,"slug":"190513148","title":"Moving Target Defense for Deep Visual Sensing against Adversarial Examples","date":"2019-05-11","arxiv_id":"1905.13148","n_code_links":0,"syntology":null},{"paper":"/paper/improved-precision-and-recall-metric-for","slug":"improved-precision-and-recall-metric-for","title":"Improved Precision and Recall Metric for Assessing Generative Models","date":"2019-04-15","arxiv_id":"1904.06991","n_code_links":10,"syntology":{"ran":30,"of":42,"n_ran_checked":20,"n_instrument":10,"unverified":12,"pointer_only":25,"phrase":"30 ran (of which 12 constructed an object rather than computing a result; 20 with no instrument failure: 5 honoured, 1 violated, 14 with no contract checked; 10 where Syntology's instrument failed) · 12 unverified","official":{"repos":["kynkaat/improved-precision-and-recall-metric"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/bayesian-neural-networks-at-finite","slug":"bayesian-neural-networks-at-finite","title":"Bayesian Neural Networks at Finite Temperature","date":"2019-04-08","arxiv_id":"1904.04154","n_code_links":1,"syntology":null},{"paper":null,"slug":"sound-source-ranging-using-a-feed-forward","title":"Sound source ranging using a feed-forward neural network with fitting-based early stopping","date":"2019-04-01","arxiv_id":"1904.00583","n_code_links":0,"syntology":null},{"paper":"/paper/gradient-descent-with-early-stopping-is","slug":"gradient-descent-with-early-stopping-is","title":"Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks","date":"2019-03-27","arxiv_id":"1903.11680","n_code_links":1,"syntology":null},{"paper":null,"slug":"implicit-regularization-via-hadamard-product","title":"High-Dimensional Linear Regression via Implicit Regularization","date":"2019-03-22","arxiv_id":"1903.09367","n_code_links":0,"syntology":null},{"paper":"/paper/high-fidelity-image-generation-with-fewer","slug":"high-fidelity-image-generation-with-fewer","title":"High-Fidelity Image Generation With Fewer Labels","date":"2019-03-06","arxiv_id":"1903.02271","n_code_links":1,"syntology":null},{"paper":"/paper/neural-persistence-a-complexity-measure-for","slug":"neural-persistence-a-complexity-measure-for","title":"Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology","date":"2018-12-23","arxiv_id":"1812.09764","n_code_links":2,"syntology":{"ran":10,"of":13,"n_ran_checked":9,"n_instrument":1,"unverified":3,"pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["BorgwardtLab/Neural-Persistence"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deepcalib-a-deep-learning-approach-for","slug":"deepcalib-a-deep-learning-approach-for","title":"DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras","date":"2018-12-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/pitfalls-of-graph-neural-network-evaluation","slug":"pitfalls-of-graph-neural-network-evaluation","title":"Pitfalls of Graph Neural Network Evaluation","date":"2018-11-14","arxiv_id":"1811.05868","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"fast-hyperparameter-optimization-of-deep","title":"Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates","date":"2018-11-06","arxiv_id":"1811.02319","n_code_links":0,"syntology":null},{"paper":null,"slug":"metropolis-hastings-view-on-variational","title":"Metropolis-Hastings view on variational inference and adversarial training","date":"2018-10-16","arxiv_id":"1810.07151","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-dynamic-approach-to-sparse-model","title":"A Unified Dynamic Approach to Sparse Model Selection","date":"2018-10-08","arxiv_id":"1810.03608","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-gan-training-for-high-fidelity","slug":"large-scale-gan-training-for-high-fidelity","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","date":"2018-09-28","arxiv_id":"1809.11096","n_code_links":35,"syntology":{"ran":28,"of":41,"n_ran_checked":20,"n_instrument":8,"unverified":13,"pointer_only":14,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 1 honoured, 2 violated, 17 with no contract checked; 8 where Syntology's instrument failed) · 13 unverified","official":null}},{"paper":null,"slug":"an-analytic-theory-of-generalization-dynamics","title":"An analytic theory of generalization dynamics and transfer learning in deep linear networks","date":"2018-09-27","arxiv_id":"1809.10374","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-collaborative-approach-to-angel-and-venture","title":"A Collaborative Approach to Angel and Venture Capital Investment Recommendations","date":"2018-07-26","arxiv_id":"1807.09967","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalization-bounds-for-unsupervised-cross","title":"Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs","date":"2018-07-23","arxiv_id":"1807.08501","n_code_links":0,"syntology":null},{"paper":null,"slug":"minnorm-training-an-algorithm-for-training","title":"Minnorm training: an algorithm for training over-parameterized deep neural networks","date":"2018-06-03","arxiv_id":"1806.00730","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-dynamics-of-learning-a-random-matrix","title":"The Dynamics of Learning: A Random Matrix Approach","date":"2018-05-30","arxiv_id":"1805.11917","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-stopping-for-nonparametric-testing","title":"Early Stopping for Nonparametric Testing","date":"2018-05-25","arxiv_id":"1805.09950","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-importance-of-norm-regularization-in","title":"The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration","date":"2018-02-10","arxiv_id":"1802.03560","n_code_links":0,"syntology":null},{"paper":null,"slug":"tesla-task-wise-early-stopping-and-loss","title":"TESLA: Task-wise Early Stopping and Loss Aggregation for Dynamic Neural Network Inference","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"theory-of-deep-learning-iii-explaining-the","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle","date":"2017-12-30","arxiv_id":"1801.00173","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-robust-deep-neural-networks-for-road","title":"Building Robust Deep Neural Networks for Road Sign Detection","date":"2017-12-26","arxiv_id":"1712.09327","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-particle-gradient-descent-for","title":"Stochastic Particle Gradient Descent for Infinite Ensembles","date":"2017-12-14","arxiv_id":"1712.05438","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-dimensional-dynamics-of-generalization","title":"High-dimensional dynamics of generalization error in neural networks","date":"2017-10-10","arxiv_id":"1710.03667","n_code_links":0,"syntology":null},{"paper":null,"slug":"massively-parallel-feature-selection-for-big","title":"Massively-Parallel Feature Selection for Big Data","date":"2017-08-23","arxiv_id":"1708.07178","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimization-by-gradient-boosting","title":"Optimization by gradient boosting","date":"2017-07-17","arxiv_id":"1707.05023","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-using-deep-cnns-trained-on","title":"Object Detection Using Deep CNNs Trained on Synthetic Images","date":"2017-06-21","arxiv_id":"1706.06782","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-optimal-run-racing-application-to-deep","title":"Toward Optimal Run Racing: Application to Deep Learning Calibration","date":"2017-06-10","arxiv_id":"1706.03199","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-neural-architecture-search-using","slug":"accelerating-neural-architecture-search-using","title":"Accelerating Neural Architecture Search using Performance Prediction","date":"2017-05-30","arxiv_id":"1705.10823","n_code_links":2,"syntology":null},{"paper":"/paper/regularizing-model-complexity-and-label","slug":"regularizing-model-complexity-and-label","title":"Regularizing Model Complexity and Label Structure for Multi-Label Text Classification","date":"2017-05-01","arxiv_id":"1705.00740","n_code_links":1,"syntology":null},{"paper":"/paper/google-vizier-a-service-for-black-box","slug":"google-vizier-a-service-for-black-box","title":"Google Vizier: A Service for Black-Box Optimization","date":"2017-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"boosted-sparse-non-linear-distance-metric","title":"Boosted Sparse Non-linear Distance Metric Learning","date":"2015-12-10","arxiv_id":"1512.03396","n_code_links":0,"syntology":null},{"paper":"/paper/nytro-when-subsampling-meets-early-stopping","slug":"nytro-when-subsampling-meets-early-stopping","title":"NYTRO: When Subsampling Meets Early Stopping","date":"2015-10-19","arxiv_id":"1510.05684","n_code_links":1,"syntology":null},{"paper":"/paper/how-to-generate-a-good-word-embedding","slug":"how-to-generate-a-good-word-embedding","title":"How to Generate a Good Word Embedding?","date":"2015-07-20","arxiv_id":"1507.05523","n_code_links":2,"syntology":null},{"paper":null,"slug":"totally-corrective-boosting-with-cardinality","title":"Totally Corrective Boosting with Cardinality Penalization","date":"2015-04-07","arxiv_id":"1504.01446","n_code_links":0,"syntology":null},{"paper":"/paper/early-stopping-is-nonparametric-variational","slug":"early-stopping-is-nonparametric-variational","title":"Early Stopping is Nonparametric Variational Inference","date":"2015-04-06","arxiv_id":"1504.01344","n_code_links":1,"syntology":null},{"paper":null,"slug":"compute-less-to-get-more-using-orc-to-improve","title":"Compute Less to Get More: Using ORC to Improve Sparse Filtering","date":"2014-09-16","arxiv_id":"1409.4689","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonconvex-statistical-optimization-minimax","title":"Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time","date":"2014-08-22","arxiv_id":"1408.5352","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximated-infomax-early-stopping","title":"Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images","date":"2013-12-19","arxiv_id":"1312.5412","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-stopping-and-non-parametric-regression","title":"Early stopping and non-parametric regression: An optimal data-dependent stopping rule","date":"2013-06-15","arxiv_id":"1306.3574","n_code_links":0,"syntology":null}],"record_sha256":"3ffdbc1843066de36e79e92008dad5aa1afbb60e68b212c1fc49eaa5f90ef452","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}