{"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/deep-learning/papers/6","list_of":"/task/deep-learning","task":"Deep 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":6,"pages_in_order":95,"rows_per_page":100,"rows":[501,600],"of":9423,"counts":{"archive_papers_tagged":9423,"with_a_code_link":2693,"where_syntology_ran_a_sample":410,"not_listed_spam_title":0,"listed":9423,"listed_where_code_ran":410,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":356,"every_run_a_failure_of_syntologys_instrument":54,"listed_with_a_run_with_no_instrument_failure":356,"listed_every_run_a_failure_of_syntologys_instrument":54,"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/deep-learning","prev":"/task/deep-learning/papers/5","next":"/task/deep-learning/papers/7","papers":[{"url":"/paper/a-review-on-deep-learning-techniques-applied","slug":"a-review-on-deep-learning-techniques-applied","title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","date":"2017-04-22","arxiv_id":"1704.06857","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-decentralized-parking-lot","slug":"deep-learning-for-decentralized-parking-lot","title":"Deep learning for decentralized parking lot occupancy detection","date":"2017-04-15","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/knowledge-transfer-for-melanoma-screening","slug":"knowledge-transfer-for-melanoma-screening","title":"Knowledge Transfer for Melanoma Screening with Deep Learning","date":"2017-03-22","arxiv_id":"1703.07479","repositories_listed":2,"syntology":null},{"url":"/paper/qmdp-net-deep-learning-for-planning-under","slug":"qmdp-net-deep-learning-for-planning-under","title":"QMDP-Net: Deep Learning for Planning under Partial Observability","date":"2017-03-20","arxiv_id":"1703.06692","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-applied-to-nlp","slug":"deep-learning-applied-to-nlp","title":"Deep Learning applied to NLP","date":"2017-03-09","arxiv_id":"1703.03091","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-with-dynamic-computation-graphs","slug":"deep-learning-with-dynamic-computation-graphs","title":"Deep Learning with Dynamic Computation Graphs","date":"2017-02-07","arxiv_id":"1702.02181","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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) · 0 unverified","sample_list":"/paper/deep-learning-with-dynamic-computation-graphs#ran","syntology_url":"https://syntology.ai/paper/1702.02181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.02181"}},"official":{"repos":["tensorflow/fold"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hashnet-deep-learning-to-hash-by-continuation","slug":"hashnet-deep-learning-to-hash-by-continuation","title":"HashNet: Deep Learning to Hash by Continuation","date":"2017-02-02","arxiv_id":"1702.00758","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/hashnet-deep-learning-to-hash-by-continuation#ran","syntology_url":"https://syntology.ai/paper/1702.00758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.00758"}},"official":{"repos":["thuml/HashNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-learning-for-ecg-classification","slug":"deep-learning-for-ecg-classification","title":"Deep Learning for ECG Classification","date":"2017-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/low-effort-place-recognition-with-wifi","slug":"low-effort-place-recognition-with-wifi","title":"Low-effort place recognition with WiFi fingerprints using deep learning","date":"2016-11-07","arxiv_id":"1611.02049","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-human-mind-for-automated-visual","slug":"deep-learning-human-mind-for-automated-visual","title":"Deep Learning Human Mind for Automated Visual Classification","date":"2016-09-01","arxiv_id":"1609.00344","repositories_listed":2,"syntology":null},{"url":"/paper/crowdnet-a-deep-convolutional-network-for","slug":"crowdnet-a-deep-convolutional-network-for","title":"CrowdNet: A Deep Convolutional Network for Dense Crowd Counting","date":"2016-08-22","arxiv_id":"1608.06197","repositories_listed":2,"syntology":null},{"url":"/paper/characterizing-driving-styles-with-deep","slug":"characterizing-driving-styles-with-deep","title":"Characterizing Driving Styles with Deep Learning","date":"2016-07-13","arxiv_id":"1607.03611","repositories_listed":2,"syntology":null},{"url":"/paper/applying-deep-learning-to-the-newsvendor","slug":"applying-deep-learning-to-the-newsvendor","title":"Applying Deep Learning to the Newsvendor Problem","date":"2016-07-07","arxiv_id":"1607.02177","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-music","slug":"deep-learning-for-music","title":"Deep Learning for Music","date":"2016-06-15","arxiv_id":"1606.04930","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-convolutional-networks-for","slug":"deep-learning-convolutional-networks-for","title":"Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation","date":"2016-06-08","arxiv_id":"1606.02382","repositories_listed":2,"syntology":null},{"url":"/paper/deep-multi-task-representation-learning-a","slug":"deep-multi-task-representation-learning-a","title":"Deep Multi-task Representation Learning: A Tensor Factorisation Approach","date":"2016-05-20","arxiv_id":"1605.06391","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-multi-task-representation-learning-a#ran","syntology_url":"https://syntology.ai/paper/1605.06391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.06391"}},"official":{"repos":["wOOL/DMTRL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-saliency-with-encoded-low-level-distance","slug":"deep-saliency-with-encoded-low-level-distance","title":"Deep Saliency with Encoded Low level Distance Map and High Level Features","date":"2016-04-19","arxiv_id":"1604.05495","repositories_listed":2,"syntology":null},{"url":"/paper/structured-and-efficient-variational-deep","slug":"structured-and-efficient-variational-deep","title":"Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors","date":"2016-03-15","arxiv_id":"1603.04733","repositories_listed":2,"syntology":null},{"url":"/paper/towards-perspective-free-object-counting-with","slug":"towards-perspective-free-object-counting-with","title":"Towards perspective-free object counting with deep learning","date":"2016-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/structural-rnn-deep-learning-on-spatio","slug":"structural-rnn-deep-learning-on-spatio","title":"Structural-RNN: Deep Learning on Spatio-Temporal Graphs","date":"2015-11-17","arxiv_id":"1511.05298","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/structural-rnn-deep-learning-on-spatio#ran","syntology_url":"https://syntology.ai/paper/1511.05298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.05298"}},"official":{"repos":["asheshjain399/RNNexp"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/lstm-based-deep-learning-models-for-non","slug":"lstm-based-deep-learning-models-for-non","title":"LSTM-based Deep Learning Models for Non-factoid Answer Selection","date":"2015-11-12","arxiv_id":"1511.04108","repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-deep-learning-for-robust-rgb-d","slug":"multimodal-deep-learning-for-robust-rgb-d","title":"Multimodal Deep Learning for Robust RGB-D Object Recognition","date":"2015-07-24","arxiv_id":"1507.06821","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-and-the-information-bottleneck","slug":"deep-learning-and-the-information-bottleneck","title":"Deep Learning and the Information Bottleneck Principle","date":"2015-03-09","arxiv_id":"1503.02406","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-with-limited-numerical","slug":"deep-learning-with-limited-numerical","title":"Deep Learning with Limited Numerical Precision","date":"2015-02-09","arxiv_id":"1502.02551","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-answer-sentence-selection","slug":"deep-learning-for-answer-sentence-selection","title":"Deep Learning for Answer Sentence Selection","date":"2014-12-04","arxiv_id":"1412.1632","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-face-attributes-in-the-wild","slug":"deep-learning-face-attributes-in-the-wild","title":"Deep Learning Face Attributes in the Wild","date":"2014-11-28","arxiv_id":"1411.7766","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-learning-face-attributes-in-the-wild#ran","syntology_url":"https://syntology.ai/paper/1411.7766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1411.7766"}},"official":null}},{"url":"/paper/pcanet-a-simple-deep-learning-baseline-for","slug":"pcanet-a-simple-deep-learning-baseline-for","title":"PCANet: A Simple Deep Learning Baseline for Image Classification?","date":"2014-04-14","arxiv_id":"1404.3606","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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","sample_list":"/paper/pcanet-a-simple-deep-learning-baseline-for#ran","syntology_url":"https://syntology.ai/paper/1404.3606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1404.3606"}},"official":null}},{"url":"/paper/generalized-adaptive-transfer-network","slug":"generalized-adaptive-transfer-network","title":"Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains","date":"2025-07-02","arxiv_id":"2507.03026","repositories_listed":1,"syntology":null},{"url":"/paper/a-hierarchical-deep-learning-approach-for","slug":"a-hierarchical-deep-learning-approach-for","title":"A Hierarchical Deep Learning Approach for Minority Instrument Detection","date":"2025-06-26","arxiv_id":"2506.21167","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-action-duration-with-contextual","slug":"adaptive-action-duration-with-contextual","title":"Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments","date":"2025-06-17","arxiv_id":"2507.00030","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-neuro-argumentative-learning","slug":"object-centric-neuro-argumentative-learning","title":"Object-Centric Neuro-Argumentative Learning","date":"2025-06-17","arxiv_id":"2506.14577","repositories_listed":1,"syntology":null},{"url":"/paper/efficiency-robustness-of-dynamic-deep","slug":"efficiency-robustness-of-dynamic-deep","title":"Efficiency Robustness of Dynamic Deep Learning Systems","date":"2025-06-12","arxiv_id":"2506.10831","repositories_listed":1,"syntology":null},{"url":"/paper/2506-08600","slug":"2506-08600","title":"CALT: A Library for Computer Algebra with Transformer","date":"2025-06-10","arxiv_id":"2506.08600","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-interpretable-deep-learning-with-lies","slug":"sparse-interpretable-deep-learning-with-lies","title":"Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression","date":"2025-06-09","arxiv_id":"2506.08267","repositories_listed":1,"syntology":null},{"url":"/paper/efficientfer-efficientnetv2-based-deep","slug":"efficientfer-efficientnetv2-based-deep","title":"EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition","date":"2025-06-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gibbs-randomness-compression-proposition-an","slug":"gibbs-randomness-compression-proposition-an","title":"Gibbs randomness-compression proposition: An efficient deep learning","date":"2025-05-29","arxiv_id":"2505.23869","repositories_listed":1,"syntology":null},{"url":"/paper/position-adopt-constraints-over-penalties-in","slug":"position-adopt-constraints-over-penalties-in","title":"Position: Adopt Constraints Over Penalties in Deep Learning","date":"2025-05-27","arxiv_id":"2505.20628","repositories_listed":1,"syntology":null},{"url":"/paper/wildfire-spread-forecasting-with-deep","slug":"wildfire-spread-forecasting-with-deep","title":"Wildfire spread forecasting with Deep Learning","date":"2025-05-23","arxiv_id":"2505.17556","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-driven-ultra-high-definition","slug":"deep-learning-driven-ultra-high-definition","title":"Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey","date":"2025-05-22","arxiv_id":"2505.16161","repositories_listed":1,"syntology":null},{"url":"/paper/are-the-confidence-scores-of-reviewers","slug":"are-the-confidence-scores-of-reviewers","title":"Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI","date":"2025-05-21","arxiv_id":"2505.15031","repositories_listed":1,"syntology":null},{"url":"/paper/kerneloracle-predicting-the-linux-scheduler-s","slug":"kerneloracle-predicting-the-linux-scheduler-s","title":"KernelOracle: Predicting the Linux Scheduler's Next Move with Deep Learning","date":"2025-05-21","arxiv_id":"2505.15213","repositories_listed":1,"syntology":null},{"url":"/paper/physics-guided-learning-of-meteorological","slug":"physics-guided-learning-of-meteorological","title":"Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting","date":"2025-05-20","arxiv_id":"2505.14555","repositories_listed":1,"syntology":null},{"url":"/paper/the-role-of-data-partitioning-on-the","slug":"the-role-of-data-partitioning-on-the","title":"The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study","date":"2025-05-19","arxiv_id":"2505.13021","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11190","slug":"2505-11190","title":"JaxSGMC: Modular stochastic gradient MCMC in JAX","date":"2025-05-16","arxiv_id":"2505.11190","repositories_listed":1,"syntology":null},{"url":"/paper/2505-10665","slug":"2505-10665","title":"Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model","date":"2025-05-15","arxiv_id":"2505.10665","repositories_listed":1,"syntology":null},{"url":"/paper/2505-10704","slug":"2505-10704","title":"ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data","date":"2025-05-15","arxiv_id":"2505.10704","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":2,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/2505-10704#ran","syntology_url":"https://syntology.ai/paper/2505.10704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.10704"}},"official":{"repos":["gmum/zeus"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/zero-shot-multi-modal-large-language-model-v","slug":"zero-shot-multi-modal-large-language-model-v","title":"Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping","date":"2025-05-14","arxiv_id":"2505.09252","repositories_listed":1,"syntology":null},{"url":"/paper/physics-assisted-and-topology-informed-deep","slug":"physics-assisted-and-topology-informed-deep","title":"Physics-Assisted and Topology-Informed Deep Learning for Weather Prediction","date":"2025-05-08","arxiv_id":"2505.04918","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-reproducibility-of-deep","slug":"improving-the-reproducibility-of-deep","title":"Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis","date":"2025-05-06","arxiv_id":"2505.03165","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-boundary-detection-in-deep-1","slug":"rethinking-boundary-detection-in-deep-1","title":"Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation","date":"2025-05-06","arxiv_id":"2505.04652","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-design-choices-for-autoregressive","slug":"exploring-design-choices-for-autoregressive","title":"Exploring Design Choices for Autoregressive Deep Learning Climate Models","date":"2025-05-05","arxiv_id":"2505.02506","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-design-choices-for-autoregressive#ran","syntology_url":"https://syntology.ai/paper/2505.02506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.02506"}},"official":{"repos":["LSX-UniWue/dl-climate-models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/wide-deep-learning-for-node-classification","slug":"wide-deep-learning-for-node-classification","title":"Wide & Deep Learning for Node Classification","date":"2025-05-04","arxiv_id":"2505.02020","repositories_listed":1,"syntology":null},{"url":"/paper/autonomous-embodied-agents-when-robotics","slug":"autonomous-embodied-agents-when-robotics","title":"Autonomous Embodied Agents: When Robotics Meets Deep Learning Reasoning","date":"2025-05-02","arxiv_id":"2505.00935","repositories_listed":1,"syntology":null},{"url":"/paper/geolocating-earth-imagery-from-iss","slug":"geolocating-earth-imagery-from-iss","title":"Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping","date":"2025-04-29","arxiv_id":"2504.21194","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparison-of-generative-deep-learning","slug":"a-comparison-of-generative-deep-learning","title":"A comparison of generative deep learning methods for multivariate angular simulation","date":"2025-04-28","arxiv_id":"2504.21505","repositories_listed":1,"syntology":null},{"url":"/paper/stcl-curriculum-learning-strategies-for-deep","slug":"stcl-curriculum-learning-strategies-for-deep","title":"STCL:Curriculum learning Strategies for deep learning image steganography models","date":"2025-04-24","arxiv_id":"2504.17609","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-with-missing-data","slug":"deep-learning-with-missing-data","title":"Deep learning with missing data","date":"2025-04-21","arxiv_id":"2504.15388","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-based-supervised-transfer","slug":"a-deep-learning-based-supervised-transfer","title":"A Deep Learning-Based Supervised Transfer Learning Framework for DOA Estimation with Array Imperfections","date":"2025-04-18","arxiv_id":"2504.13394","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-automatic-cad-annotations-for","slug":"leveraging-automatic-cad-annotations-for","title":"Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding","date":"2025-04-18","arxiv_id":"2504.13580","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-computing-supported-adversarial","slug":"quantum-computing-supported-adversarial","title":"Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification","date":"2025-04-17","arxiv_id":"2504.12644","repositories_listed":1,"syntology":null},{"url":"/paper/readable-twins-of-unreadable-models","slug":"readable-twins-of-unreadable-models","title":"Readable Twins of Unreadable Models","date":"2025-04-17","arxiv_id":"2504.13150","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-in-concealed-dense-prediction","slug":"deep-learning-in-concealed-dense-prediction","title":"Deep Learning in Concealed Dense Prediction","date":"2025-04-15","arxiv_id":"2504.10979","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-methods-for-detecting-thermal","slug":"deep-learning-methods-for-detecting-thermal","title":"Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines","date":"2025-04-11","arxiv_id":"2504.08632","repositories_listed":1,"syntology":null},{"url":"/paper/protoecgnet-case-based-interpretable-deep","slug":"protoecgnet-case-based-interpretable-deep","title":"ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning","date":"2025-04-11","arxiv_id":"2504.08713","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-relational-deep-learning-with","slug":"boosting-relational-deep-learning-with","title":"Boosting Relational Deep Learning with Pretrained Tabular Models","date":"2025-04-07","arxiv_id":"2504.04934","repositories_listed":1,"syntology":null},{"url":"/paper/climplicit-climatic-implicit-embeddings-for","slug":"climplicit-climatic-implicit-embeddings-for","title":"Climplicit: Climatic Implicit Embeddings for Global Ecological Tasks","date":"2025-04-07","arxiv_id":"2504.05089","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/climplicit-climatic-implicit-embeddings-for#ran","syntology_url":"https://syntology.ai/paper/2504.05089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.05089"}},"official":{"repos":["ecovision-uzh/climplicit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cooper-a-library-for-constrained-optimization","slug":"cooper-a-library-for-constrained-optimization","title":"Cooper: A Library for Constrained Optimization in Deep Learning","date":"2025-04-01","arxiv_id":"2504.01212","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cooper-a-library-for-constrained-optimization#ran","syntology_url":"https://syntology.ai/paper/2504.01212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.01212"}},"official":{"repos":["cooper-org/cooper"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-up-resonate-and-fire-networks-for","slug":"scaling-up-resonate-and-fire-networks-for","title":"Scaling Up Resonate-and-Fire Networks for Fast Deep Learning","date":"2025-04-01","arxiv_id":"2504.00719","repositories_listed":1,"syntology":null},{"url":"/paper/eap4emsig-enhancing-event-driven-microscopy","slug":"eap4emsig-enhancing-event-driven-microscopy","title":"EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis","date":"2025-03-30","arxiv_id":"2504.00047","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-benchmark-for-rna-3d","slug":"a-comprehensive-benchmark-for-rna-3d","title":"A Comprehensive Benchmark for RNA 3D Structure-Function Modeling","date":"2025-03-27","arxiv_id":"2503.21681","repositories_listed":1,"syntology":null},{"url":"/paper/data-poisoning-in-deep-learning-a-survey","slug":"data-poisoning-in-deep-learning-a-survey","title":"Data Poisoning in Deep Learning: A Survey","date":"2025-03-27","arxiv_id":"2503.22759","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-forensic-identification-of","slug":"deep-learning-for-forensic-identification-of","title":"Deep Learning for Forensic Identification of Source","date":"2025-03-26","arxiv_id":"2503.20994","repositories_listed":1,"syntology":null},{"url":"/paper/face-spoofing-detection-using-deep-learning","slug":"face-spoofing-detection-using-deep-learning","title":"Face Spoofing Detection using Deep Learning","date":"2025-03-25","arxiv_id":"2503.19223","repositories_listed":1,"syntology":null},{"url":"/paper/omnilearn-a-framework-for-distributed-deep","slug":"omnilearn-a-framework-for-distributed-deep","title":"OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters","date":"2025-03-21","arxiv_id":"2503.17469","repositories_listed":1,"syntology":null},{"url":"/paper/cost-effective-deep-learning-infrastructure","slug":"cost-effective-deep-learning-infrastructure","title":"Cost-effective Deep Learning Infrastructure with NVIDIA GPU","date":"2025-03-14","arxiv_id":"2503.11246","repositories_listed":1,"syntology":null},{"url":"/paper/label-unbalance-in-high-frequency-trading","slug":"label-unbalance-in-high-frequency-trading","title":"Label Unbalance in High-frequency Trading","date":"2025-03-13","arxiv_id":"2503.09988","repositories_listed":1,"syntology":null},{"url":"/paper/self-consistent-equation-guided-neural","slug":"self-consistent-equation-guided-neural","title":"Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data","date":"2025-03-12","arxiv_id":"2503.09097","repositories_listed":1,"syntology":null},{"url":"/paper/terrier-a-deep-learning-repeat-classifier","slug":"terrier-a-deep-learning-repeat-classifier","title":"Terrier: A Deep Learning Repeat Classifier","date":"2025-03-12","arxiv_id":"2503.09312","repositories_listed":1,"syntology":null},{"url":"/paper/oasis-one-image-is-all-you-need-for","slug":"oasis-one-image-is-all-you-need-for","title":"Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis","date":"2025-03-11","arxiv_id":"2503.08741","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/oasis-one-image-is-all-you-need-for#ran","syntology_url":"https://syntology.ai/paper/2503.08741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.08741"}},"official":{"repos":["Letian2003/MM_INF"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-powered-electrical-brain","slug":"deep-learning-powered-electrical-brain","title":"Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics","date":"2025-02-24","arxiv_id":"2502.17213","repositories_listed":1,"syntology":null},{"url":"/paper/liver-cirrhosis-stage-estimation-from-mri","slug":"liver-cirrhosis-stage-estimation-from-mri","title":"Liver Cirrhosis Stage Estimation from MRI with Deep Learning","date":"2025-02-23","arxiv_id":"2502.18225","repositories_listed":1,"syntology":null},{"url":"/paper/pdeeppp-a-deep-learning-framework-with","slug":"pdeeppp-a-deep-learning-framework-with","title":"A general language model for peptide identification","date":"2025-02-21","arxiv_id":"2502.15610","repositories_listed":1,"syntology":null},{"url":"/paper/tabmixer-advancing-tabular-data-analysis-with","slug":"tabmixer-advancing-tabular-data-analysis-with","title":"TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach","date":"2025-02-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/trusworthy-toward-clinically-applicable-deep","slug":"trusworthy-toward-clinically-applicable-deep","title":"TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound","date":"2025-02-20","arxiv_id":"2502.14707","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-framework-for-efficient","slug":"a-deep-learning-framework-for-efficient","title":"A deep learning framework for efficient pathology image analysis","date":"2025-02-18","arxiv_id":"2502.13027","repositories_listed":1,"syntology":null},{"url":"/paper/epidemic-guided-deep-learning-for","slug":"epidemic-guided-deep-learning-for","title":"Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak","date":"2025-02-15","arxiv_id":"2502.10786","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-and-tracking","slug":"object-detection-and-tracking","title":"Object Detection and Tracking","date":"2025-02-14","arxiv_id":"2502.10310","repositories_listed":1,"syntology":null},{"url":"/paper/reconstruction-of-frequency-localized","slug":"reconstruction-of-frequency-localized","title":"Reconstruction of frequency-localized functions from pointwise samples via least squares and deep learning","date":"2025-02-13","arxiv_id":"2502.09794","repositories_listed":1,"syntology":null},{"url":"/paper/survey-on-single-image-reflection-removal","slug":"survey-on-single-image-reflection-removal","title":"Survey on Single-Image Reflection Removal using Deep Learning Techniques","date":"2025-02-12","arxiv_id":"2502.08836","repositories_listed":1,"syntology":null},{"url":"/paper/calibrating-llms-with-information-theoretic","slug":"calibrating-llms-with-information-theoretic","title":"Calibrating LLMs with Information-Theoretic Evidential Deep Learning","date":"2025-02-10","arxiv_id":"2502.06351","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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) · 0 unverified","sample_list":"/paper/calibrating-llms-with-information-theoretic#ran","syntology_url":"https://syntology.ai/paper/2502.06351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06351"}},"official":{"repos":["sandylaker/ib-edl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/relgnn-composite-message-passing-for","slug":"relgnn-composite-message-passing-for","title":"RelGNN: Composite Message Passing for Relational Deep Learning","date":"2025-02-10","arxiv_id":"2502.06784","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/relgnn-composite-message-passing-for#ran","syntology_url":"https://syntology.ai/paper/2502.06784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06784"}},"official":{"repos":["snap-stanford/relgnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fetdtialign-a-deep-learning-framework-for","slug":"fetdtialign-a-deep-learning-framework-for","title":"FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI","date":"2025-02-03","arxiv_id":"2502.01057","repositories_listed":1,"syntology":null},{"url":"/paper/input-layer-regularization-and-automated","slug":"input-layer-regularization-and-automated","title":"Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learning","date":"2025-01-30","arxiv_id":"2501.18074","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-functional-brain-connectome","slug":"rethinking-functional-brain-connectome","title":"Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?","date":"2025-01-28","arxiv_id":"2501.17207","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-functional-brain-connectome#ran","syntology_url":"https://syntology.ai/paper/2501.17207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.17207"}},"official":{"repos":["learningkeqi/rethinkingbca"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/end-to-end-localized-deep-learning-for-cryo","slug":"end-to-end-localized-deep-learning-for-cryo","title":"End-to-end localized deep learning for Cryo-ET","date":"2025-01-25","arxiv_id":"2501.15246","repositories_listed":1,"syntology":null},{"url":"/paper/gentl-a-general-transfer-learning-model-for","slug":"gentl-a-general-transfer-learning-model-for","title":"GenTL: A General Transfer Learning Model for Building Thermal Dynamics","date":"2025-01-23","arxiv_id":"2501.13703","repositories_listed":1,"syntology":null},{"url":"/paper/quantification-via-gaussian-latent-space","slug":"quantification-via-gaussian-latent-space","title":"Quantification via Gaussian Latent Space Representations","date":"2025-01-23","arxiv_id":"2501.13638","repositories_listed":1,"syntology":null},{"url":"/paper/utilising-deep-learning-to-elicit-expert","slug":"utilising-deep-learning-to-elicit-expert","title":"Utilising Deep Learning to Elicit Expert Uncertainty","date":"2025-01-21","arxiv_id":"2501.11813","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-early-alzheimer-disease","slug":"deep-learning-for-early-alzheimer-disease","title":"Deep Learning for Early Alzheimer Disease Detection with MRI Scans","date":"2025-01-17","arxiv_id":"2501.09999","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-marvels-of-deep-learning-in","slug":"multimodal-marvels-of-deep-learning-in","title":"Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection","date":"2025-01-16","arxiv_id":"2501.09506","repositories_listed":1,"syntology":null}],"record_sha256":"f0eda06c780dee44c9337f205f8066cd0823dd908c9caeae3987bd2690f2a8c7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}