{"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/representation-learning/papers/ran/15","list_of":"/task/representation-learning","task":"Representation 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":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":15,"pages_in_order":15,"rows_per_page":100,"rows":[1401,1439],"of":1439,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"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/representation-learning/papers/ran/1","prev":"/task/representation-learning/papers/ran/14","next":null,"papers":[{"url":"/paper/tile2vec-unsupervised-representation-learning","slug":"tile2vec-unsupervised-representation-learning","title":"Tile2Vec: Unsupervised representation learning for spatially distributed data","date":"2018-05-08","arxiv_id":"1805.02855","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/tile2vec-unsupervised-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1805.02855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.02855"}},"official":{"repos":["ermongroup/tile2vec"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-disentangled-representation","slug":"unsupervised-disentangled-representation","title":"Unsupervised Disentangled Representation Learning with Analogical Relations","date":"2018-04-25","arxiv_id":"1804.09502","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/unsupervised-disentangled-representation#ran","syntology_url":"https://syntology.ai/paper/1804.09502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.09502"}},"official":{"repos":["ZejianLi/analogical-training"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-discrete-sentence-representation","slug":"unsupervised-discrete-sentence-representation","title":"Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation","date":"2018-04-22","arxiv_id":"1804.08069","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-discrete-sentence-representation#ran","syntology_url":"https://syntology.ai/paper/1804.08069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08069"}},"official":{"repos":["snakeztc/NeuralDialog-LAED"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/hyperdense-net-a-hyper-densely-connected-cnn","slug":"hyperdense-net-a-hyper-densely-connected-cnn","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","date":"2018-04-09","arxiv_id":"1804.02967","repositories_listed":3,"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/hyperdense-net-a-hyper-densely-connected-cnn#ran","syntology_url":"https://syntology.ai/paper/1804.02967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.02967"}},"official":{"repos":["josedolz/HyperDenseNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/stochastic-adversarial-video-prediction","slug":"stochastic-adversarial-video-prediction","title":"Stochastic Adversarial Video Prediction","date":"2018-04-04","arxiv_id":"1804.01523","repositories_listed":4,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/stochastic-adversarial-video-prediction#ran","syntology_url":"https://syntology.ai/paper/1804.01523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.01523"}},"official":{"repos":["alexlee-gk/video_prediction"],"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":["listed","official"]}}},{"url":"/paper/who-let-the-dogs-out-modeling-dog-behavior","slug":"who-let-the-dogs-out-modeling-dog-behavior","title":"Who Let The Dogs Out? Modeling Dog Behavior From Visual Data","date":"2018-03-28","arxiv_id":"1803.10827","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"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 0 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","sample_list":"/paper/who-let-the-dogs-out-modeling-dog-behavior#ran","syntology_url":"https://syntology.ai/paper/1803.10827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10827"}},"official":{"repos":["ehsanik/dogTorch"],"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/unsupervised-representation-learning-by-1","slug":"unsupervised-representation-learning-by-1","title":"Unsupervised Representation Learning by Predicting Image Rotations","date":"2018-03-21","arxiv_id":"1803.07728","repositories_listed":20,"syntology":{"n":22,"n_ran":16,"n_constructed":7,"n_ran_checked":14,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":18,"phrase":"16 ran (of which 7 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/unsupervised-representation-learning-by-1#ran","syntology_url":"https://syntology.ai/paper/1803.07728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07728"}},"official":{"repos":["gidariss/FeatureLearningRotNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/factorised-spatial-representation-learning","slug":"factorised-spatial-representation-learning","title":"Factorised spatial representation learning: application in semi-supervised myocardial segmentation","date":"2018-03-19","arxiv_id":"1803.07031","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/factorised-spatial-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1803.07031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07031"}},"official":{"repos":["agis85/spatial_factorisation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-efficient-framework-for-learning-sentence","slug":"an-efficient-framework-for-learning-sentence","title":"An efficient framework for learning sentence representations","date":"2018-03-07","arxiv_id":"1803.02893","repositories_listed":6,"syntology":{"n":21,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/an-efficient-framework-for-learning-sentence#ran","syntology_url":"https://syntology.ai/paper/1803.02893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.02893"}},"official":null}},{"url":"/paper/learning-adversarially-fair-and-transferable","slug":"learning-adversarially-fair-and-transferable","title":"Learning Adversarially Fair and Transferable Representations","date":"2018-02-17","arxiv_id":"1802.06309","repositories_listed":7,"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":3,"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/learning-adversarially-fair-and-transferable#ran","syntology_url":"https://syntology.ai/paper/1802.06309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.06309"}},"official":{"repos":["VectorInstitute/laftr"],"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/ai2-thor-an-interactive-3d-environment-for","slug":"ai2-thor-an-interactive-3d-environment-for","title":"AI2-THOR: An Interactive 3D Environment for Visual AI","date":"2017-12-14","arxiv_id":"1712.05474","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ai2-thor-an-interactive-3d-environment-for#ran","syntology_url":"https://syntology.ai/paper/1712.05474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.05474"}},"official":{"repos":["allenai/ai2thor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-learning-for-cell-level-visual","slug":"unsupervised-learning-for-cell-level-visual","title":"Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks","date":"2017-11-30","arxiv_id":"1711.11317","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-learning-for-cell-level-visual#ran","syntology_url":"https://syntology.ai/paper/1711.11317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.11317"}},"official":{"repos":["bohu615/nu_gan"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-deep-compositional-grammatical","slug":"learning-deep-compositional-grammatical","title":"AOGNets: Compositional Grammatical Architectures for Deep Learning","date":"2017-11-15","arxiv_id":"1711.05847","repositories_listed":4,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"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) · 5 unverified","sample_list":"/paper/learning-deep-compositional-grammatical#ran","syntology_url":"https://syntology.ai/paper/1711.05847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.05847"}},"official":{"repos":["iVMCL/AOGNets"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/neural-discrete-representation-learning","slug":"neural-discrete-representation-learning","title":"Neural Discrete Representation Learning","date":"2017-11-02","arxiv_id":"1711.00937","repositories_listed":50,"syntology":{"n":80,"n_ran":64,"n_constructed":35,"n_ran_checked":60,"n_instrument":4,"n_unverified":16,"n_honours":0,"n_violates":0,"n_no_contract":60,"n_pointer_only":32,"phrase":"64 ran (of which 35 constructed an object rather than computing a result; 60 with no instrument failure: 0 honoured, 0 violated, 60 with no contract checked; 4 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/neural-discrete-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1711.00937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.00937"}},"official":{"repos":["deepmind/sonnet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/eigenoption-discovery-through-the-deep","slug":"eigenoption-discovery-through-the-deep","title":"Eigenoption Discovery through the Deep Successor Representation","date":"2017-10-30","arxiv_id":"1710.11089","repositories_listed":1,"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/eigenoption-discovery-through-the-deep#ran","syntology_url":"https://syntology.ai/paper/1710.11089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.11089"}},"official":null}},{"url":"/paper/network-embedding-as-matrix-factorization","slug":"network-embedding-as-matrix-factorization","title":"Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec","date":"2017-10-09","arxiv_id":"1710.02971","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/network-embedding-as-matrix-factorization#ran","syntology_url":"https://syntology.ai/paper/1710.02971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.02971"}},"official":{"repos":["xptree/NetMF"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/niftynet-a-deep-learning-platform-for-medical","slug":"niftynet-a-deep-learning-platform-for-medical","title":"NiftyNet: a deep-learning platform for medical imaging","date":"2017-09-11","arxiv_id":"1709.03485","repositories_listed":10,"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/niftynet-a-deep-learning-platform-for-medical#ran","syntology_url":"https://syntology.ai/paper/1709.03485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.03485"}},"official":{"repos":["NifTK/NiftyNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/representation-learning-by-learning-to-count","slug":"representation-learning-by-learning-to-count","title":"Representation Learning by Learning to Count","date":"2017-08-22","arxiv_id":"1708.06734","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/representation-learning-by-learning-to-count#ran","syntology_url":"https://syntology.ai/paper/1708.06734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.06734"}},"official":null}},{"url":"/paper/unsupervised-representation-learning-by","slug":"unsupervised-representation-learning-by","title":"Unsupervised Representation Learning by Sorting Sequences","date":"2017-08-03","arxiv_id":"1708.01246","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-representation-learning-by#ran","syntology_url":"https://syntology.ai/paper/1708.01246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.01246"}},"official":{"repos":["HsinYingLee/OPN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph2vec-learning-distributed","slug":"graph2vec-learning-distributed","title":"graph2vec: Learning Distributed Representations of Graphs","date":"2017-07-17","arxiv_id":"1707.05005","repositories_listed":6,"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":2,"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/graph2vec-learning-distributed#ran","syntology_url":"https://syntology.ai/paper/1707.05005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.05005"}},"official":null}},{"url":"/paper/wasserstein-distance-guided-representation","slug":"wasserstein-distance-guided-representation","title":"Wasserstein Distance Guided Representation Learning for Domain Adaptation","date":"2017-07-05","arxiv_id":"1707.01217","repositories_listed":8,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wasserstein-distance-guided-representation#ran","syntology_url":"https://syntology.ai/paper/1707.01217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.01217"}},"official":{"repos":["RockySJ/WDGRL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/adversarially-regularized-autoencoders","slug":"adversarially-regularized-autoencoders","title":"Adversarially Regularized Autoencoders","date":"2017-06-13","arxiv_id":"1706.04223","repositories_listed":6,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarially-regularized-autoencoders#ran","syntology_url":"https://syntology.ai/paper/1706.04223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.04223"}},"official":{"repos":["jakezhaojb/ARAE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/inductive-representation-learning-on-large","slug":"inductive-representation-learning-on-large","title":"Inductive Representation Learning on Large Graphs","date":"2017-06-07","arxiv_id":"1706.02216","repositories_listed":20,"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/inductive-representation-learning-on-large#ran","syntology_url":"https://syntology.ai/paper/1706.02216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.02216"}},"official":{"repos":["williamleif/GraphSAGE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/poincare-embeddings-for-learning-hierarchical","slug":"poincare-embeddings-for-learning-hierarchical","title":"Poincaré Embeddings for Learning Hierarchical Representations","date":"2017-05-22","arxiv_id":"1705.08039","repositories_listed":9,"syntology":{"n":14,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":8,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/poincare-embeddings-for-learning-hierarchical#ran","syntology_url":"https://syntology.ai/paper/1705.08039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.08039"}},"official":null}},{"url":"/paper/dataset-augmentation-in-feature-space","slug":"dataset-augmentation-in-feature-space","title":"Dataset Augmentation in Feature Space","date":"2017-02-17","arxiv_id":"1702.05538","repositories_listed":3,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 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/dataset-augmentation-in-feature-space#ran","syntology_url":"https://syntology.ai/paper/1702.05538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.05538"}},"official":null}},{"url":"/paper/deep-generalized-canonical-correlation","slug":"deep-generalized-canonical-correlation","title":"Deep Generalized Canonical Correlation Analysis","date":"2017-02-08","arxiv_id":"1702.02519","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 1 unverified","sample_list":"/paper/deep-generalized-canonical-correlation#ran","syntology_url":"https://syntology.ai/paper/1702.02519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.02519"}},"official":{"repos":["bitbucket.org/adrianbenton/dgcca-py3"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"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/icarl-incremental-classifier-and","slug":"icarl-incremental-classifier-and","title":"iCaRL: Incremental Classifier and Representation Learning","date":"2016-11-23","arxiv_id":"1611.07725","repositories_listed":10,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"5 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; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/icarl-incremental-classifier-and#ran","syntology_url":"https://syntology.ai/paper/1611.07725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.07725"}},"official":{"repos":["srebuffi/iCaRL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/very-deep-convolutional-neural-networks-for","slug":"very-deep-convolutional-neural-networks-for","title":"Very Deep Convolutional Neural Networks for Raw Waveforms","date":"2016-10-01","arxiv_id":"1610.00087","repositories_listed":10,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/very-deep-convolutional-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/1610.00087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.00087"}},"official":null}},{"url":"/paper/node2vec-scalable-feature-learning-for","slug":"node2vec-scalable-feature-learning-for","title":"node2vec: Scalable Feature Learning for Networks","date":"2016-07-03","arxiv_id":"1607.00653","repositories_listed":20,"syntology":{"n":25,"n_ran":15,"n_constructed":2,"n_ran_checked":13,"n_instrument":2,"n_unverified":10,"n_honours":2,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"15 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/node2vec-scalable-feature-learning-for#ran","syntology_url":"https://syntology.ai/paper/1607.00653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.00653"}},"official":{"repos":["aditya-grover/node2vec"],"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":["listed","unlocated"]}}},{"url":"/paper/infogan-interpretable-representation-learning","slug":"infogan-interpretable-representation-learning","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","date":"2016-06-12","arxiv_id":"1606.03657","repositories_listed":38,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/infogan-interpretable-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1606.03657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.03657"}},"official":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/joint-unsupervised-learning-of-deep","slug":"joint-unsupervised-learning-of-deep","title":"Joint Unsupervised Learning of Deep Representations and Image Clusters","date":"2016-04-13","arxiv_id":"1604.03628","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"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 0 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","sample_list":"/paper/joint-unsupervised-learning-of-deep#ran","syntology_url":"https://syntology.ai/paper/1604.03628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.03628"}},"official":{"repos":["jwyang/joint-unsupervised-learning"],"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/unsupervised-learning-of-visual-1","slug":"unsupervised-learning-of-visual-1","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","date":"2016-03-30","arxiv_id":"1603.09246","repositories_listed":9,"syntology":{"n":13,"n_ran":10,"n_constructed":6,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unsupervised-learning-of-visual-1#ran","syntology_url":"https://syntology.ai/paper/1603.09246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.09246"}},"official":null}},{"url":"/paper/learning-representations-for-automatic","slug":"learning-representations-for-automatic","title":"Learning Representations for Automatic Colorization","date":"2016-03-22","arxiv_id":"1603.06668","repositories_listed":3,"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":0,"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/learning-representations-for-automatic#ran","syntology_url":"https://syntology.ai/paper/1603.06668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.06668"}},"official":{"repos":["gustavla/autocolorize"],"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/unsupervised-representation-learning-with-1","slug":"unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","arxiv_id":"1511.06434","repositories_listed":258,"syntology":{"n":219,"n_ran":128,"n_constructed":61,"n_ran_checked":111,"n_instrument":17,"n_unverified":91,"n_honours":3,"n_violates":0,"n_no_contract":108,"n_pointer_only":111,"phrase":"128 ran (of which 61 constructed an object rather than computing a result; 111 with no instrument failure: 3 honoured, 0 violated, 108 with no contract checked; 17 where Syntology's instrument failed) · 91 unverified","sample_list":"/paper/unsupervised-representation-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/1511.06434","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06434"}},"official":null}},{"url":"/paper/domain-adversarial-training-of-neural","slug":"domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","date":"2015-05-28","arxiv_id":"1505.07818","repositories_listed":37,"syntology":{"n":52,"n_ran":35,"n_constructed":11,"n_ran_checked":18,"n_instrument":17,"n_unverified":17,"n_honours":1,"n_violates":0,"n_no_contract":17,"n_pointer_only":22,"phrase":"35 ran (of which 11 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 0 violated, 17 with no contract checked; 17 where Syntology's instrument failed) · 17 unverified","sample_list":"/paper/domain-adversarial-training-of-neural#ran","syntology_url":"https://syntology.ai/paper/1505.07818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.07818"}},"official":null}},{"url":"/paper/challenges-in-representation-learning-a","slug":"challenges-in-representation-learning-a","title":"Challenges in Representation Learning: A report on three machine learning contests","date":"2013-07-01","arxiv_id":"1307.0414","repositories_listed":12,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/challenges-in-representation-learning-a#ran","syntology_url":"https://syntology.ai/paper/1307.0414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1307.0414"}},"official":null}},{"url":"/paper/representation-learning-a-review-and-new","slug":"representation-learning-a-review-and-new","title":"Representation Learning: A Review and New Perspectives","date":"2012-06-24","arxiv_id":"1206.5538","repositories_listed":6,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/representation-learning-a-review-and-new#ran","syntology_url":"https://syntology.ai/paper/1206.5538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1206.5538"}},"official":null}}],"record_sha256":"1a65bb5a22866d3d4249ea39d1746f1905a6c354bee2c263bbd2ec5e9c9a2065","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}