{"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/retrieval/papers/51","list_of":"/task/retrieval","task":"Retrieval","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":51,"pages_in_order":143,"rows_per_page":100,"rows":[5001,5100],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"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/retrieval","prev":"/task/retrieval/papers/50","next":"/task/retrieval/papers/52","papers":[{"url":"/paper/adaptive-document-retrieval-for-deep-question","slug":"adaptive-document-retrieval-for-deep-question","title":"Adaptive Document Retrieval for Deep Question Answering","date":"2018-08-20","arxiv_id":"1808.06528","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-learn-from-web-data-through-deep","slug":"learning-to-learn-from-web-data-through-deep","title":"Learning to Learn from Web Data through Deep Semantic Embeddings","date":"2018-08-20","arxiv_id":"1808.06368","repositories_listed":1,"syntology":null},{"url":"/paper/robust-compressive-phase-retrieval-via-deep","slug":"robust-compressive-phase-retrieval-via-deep","title":"Robust Compressive Phase Retrieval via Deep Generative Priors","date":"2018-08-17","arxiv_id":"1808.05854","repositories_listed":1,"syntology":null},{"url":"/paper/retrieve-and-refine-improved-sequence","slug":"retrieve-and-refine-improved-sequence","title":"Retrieve and Refine: Improved Sequence Generation Models For Dialogue","date":"2018-08-14","arxiv_id":"1808.04776","repositories_listed":1,"syntology":null},{"url":"/paper/deep-randomized-ensembles-for-metric-learning","slug":"deep-randomized-ensembles-for-metric-learning","title":"Deep Randomized Ensembles for Metric Learning","date":"2018-08-13","arxiv_id":"1808.04469","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/deep-randomized-ensembles-for-metric-learning#ran","syntology_url":"https://syntology.ai/paper/1808.04469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04469"}},"official":{"repos":["littleredxh/DREML"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/document-informed-neural-autoregressive-topic-1","slug":"document-informed-neural-autoregressive-topic-1","title":"Document Informed Neural Autoregressive Topic Models","date":"2018-08-11","arxiv_id":"1808.03793","repositories_listed":1,"syntology":null},{"url":"/paper/target-image-video-search-based-on-local","slug":"target-image-video-search-based-on-local","title":"Video Logo Retrieval based on local Features","date":"2018-08-11","arxiv_id":"1808.03735","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-represent-bilingual-dictionaries","slug":"learning-to-represent-bilingual-dictionaries","title":"Learning to Represent Bilingual Dictionaries","date":"2018-08-10","arxiv_id":"1808.03726","repositories_listed":1,"syntology":null},{"url":"/paper/on-feature-selection-and-evaluation-of","slug":"on-feature-selection-and-evaluation-of","title":"On feature selection and evaluation of transportation mode prediction strategies","date":"2018-08-09","arxiv_id":"1808.03096","repositories_listed":1,"syntology":null},{"url":"/paper/universal-perceptual-grouping","slug":"universal-perceptual-grouping","title":"Universal Perceptual Grouping","date":"2018-08-07","arxiv_id":"1808.02312","repositories_listed":1,"syntology":null},{"url":"/paper/late-fusion-of-local-indexing-and-deep","slug":"late-fusion-of-local-indexing-and-deep","title":"Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search","date":"2018-08-03","arxiv_id":"1808.01101","repositories_listed":1,"syntology":null},{"url":"/paper/a-zero-shot-framework-for-sketch-based-image","slug":"a-zero-shot-framework-for-sketch-based-image","title":"A Zero-Shot Framework for Sketch-based Image Retrieval","date":"2018-07-31","arxiv_id":"1807.11724","repositories_listed":1,"syntology":null},{"url":"/paper/deep-group-shuffling-random-walk-for-person","slug":"deep-group-shuffling-random-walk-for-person","title":"Deep Group-shuffling Random Walk for Person Re-identification","date":"2018-07-30","arxiv_id":"1807.11178","repositories_listed":1,"syntology":null},{"url":"/paper/talking-face-generation-by-adversarially","slug":"talking-face-generation-by-adversarially","title":"Talking Face Generation by Adversarially Disentangled Audio-Visual Representation","date":"2018-07-20","arxiv_id":"1807.07860","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"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","sample_list":"/paper/talking-face-generation-by-adversarially#ran","syntology_url":"https://syntology.ai/paper/1807.07860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.07860"}},"official":null}},{"url":"/paper/a-modulation-module-for-multi-task-learning","slug":"a-modulation-module-for-multi-task-learning","title":"A Modulation Module for Multi-task Learning with Applications in Image Retrieval","date":"2018-07-17","arxiv_id":"1807.06708","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/a-modulation-module-for-multi-task-learning#ran","syntology_url":"https://syntology.ai/paper/1807.06708","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06708"}},"official":{"repos":["Zhaoxiangyun/Multi-Task-Modulation-Module"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-exemplar-based-colorization","slug":"deep-exemplar-based-colorization","title":"Deep Exemplar-based Colorization","date":"2018-07-17","arxiv_id":"1807.06587","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-exemplar-based-colorization#ran","syntology_url":"https://syntology.ai/paper/1807.06587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06587"}},"official":{"repos":["msracver/Deep-Exemplar-based-Colorization"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/combining-a-context-aware-neural-network-with","slug":"combining-a-context-aware-neural-network-with","title":"Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities","date":"2018-07-16","arxiv_id":"1807.06414","repositories_listed":1,"syntology":null},{"url":"/paper/natural-language-processing-for-information","slug":"natural-language-processing-for-information","title":"Natural Language Processing for Information Extraction","date":"2018-07-06","arxiv_id":"1807.02383","repositories_listed":1,"syntology":null},{"url":"/paper/vlase-vehicle-localization-by-aggregating","slug":"vlase-vehicle-localization-by-aggregating","title":"VLASE: Vehicle Localization by Aggregating Semantic Edges","date":"2018-07-06","arxiv_id":"1807.02536","repositories_listed":1,"syntology":null},{"url":"/paper/functional-object-oriented-network","slug":"functional-object-oriented-network","title":"Functional Object-Oriented Network: Construction & Expansion","date":"2018-07-05","arxiv_id":"1807.02189","repositories_listed":1,"syntology":null},{"url":"/paper/texttopicnet-self-supervised-learning-of","slug":"texttopicnet-self-supervised-learning-of","title":"TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces","date":"2018-07-04","arxiv_id":"1807.02110","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-languages-through-images-with-deep","slug":"bridging-languages-through-images-with-deep","title":"Bridging Languages through Images with Deep Partial Canonical Correlation Analysis","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-historical-significance-of-textual","slug":"the-historical-significance-of-textual","title":"The Historical Significance of Textual Distances","date":"2018-06-30","arxiv_id":"1807.00181","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-the-query-set-on-the-evaluation-of","slug":"impact-of-the-query-set-on-the-evaluation-of","title":"Impact of the Query Set on the Evaluation of Expert Finding Systems","date":"2018-06-28","arxiv_id":"1806.10813","repositories_listed":1,"syntology":null},{"url":"/paper/handling-massive-n-gram-datasets-efficiently","slug":"handling-massive-n-gram-datasets-efficiently","title":"Handling Massive N-Gram Datasets Efficiently","date":"2018-06-25","arxiv_id":"1806.09447","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-multi-turn-conversation-with-deep","slug":"modeling-multi-turn-conversation-with-deep","title":"Modeling Multi-turn Conversation with Deep Utterance Aggregation","date":"2018-06-24","arxiv_id":"1806.09102","repositories_listed":1,"syntology":null},{"url":"/paper/an-accurate-retrieval-through-r-mac","slug":"an-accurate-retrieval-through-r-mac","title":"An accurate retrieval through R-MAC+ descriptors for landmark recognition","date":"2018-06-22","arxiv_id":"1806.08565","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-on-the-names-of-points-of","slug":"an-empirical-study-on-the-names-of-points-of","title":"An empirical study on the names of points of interest and their changes with geographic distance","date":"2018-06-21","arxiv_id":"1806.08040","repositories_listed":1,"syntology":null},{"url":"/paper/bingan-learning-compact-binary-descriptors","slug":"bingan-learning-compact-binary-descriptors","title":"BinGAN: Learning Compact Binary Descriptors with a Regularized GAN","date":"2018-06-18","arxiv_id":"1806.06778","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-nearest-neighbors-search-for-large","slug":"efficient-nearest-neighbors-search-for-large","title":"Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition","date":"2018-06-15","arxiv_id":"1806.05946","repositories_listed":1,"syntology":null},{"url":"/paper/image-classification-and-retrieval-with","slug":"image-classification-and-retrieval-with","title":"Image classification and retrieval with random depthwise signed convolutional neural networks","date":"2018-06-15","arxiv_id":"1806.05789","repositories_listed":1,"syntology":null},{"url":"/paper/indoor-visual-positioning-aided-by-cnn-based","slug":"indoor-visual-positioning-aided-by-cnn-based","title":"Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free","date":"2018-06-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-deep-image-hashing-through","slug":"weakly-supervised-deep-image-hashing-through","title":"Weakly Supervised Deep Image Hashing through Tag Embeddings","date":"2018-06-15","arxiv_id":"1806.05804","repositories_listed":1,"syntology":null},{"url":"/paper/openedgar-open-source-software-for-sec-edgar","slug":"openedgar-open-source-software-for-sec-edgar","title":"OpenEDGAR: Open Source Software for SEC EDGAR Analysis","date":"2018-06-13","arxiv_id":"1806.04973","repositories_listed":1,"syntology":null},{"url":"/paper/named-entity-recognition-with-extremely","slug":"named-entity-recognition-with-extremely","title":"Named Entity Recognition with Extremely Limited Data","date":"2018-06-12","arxiv_id":"1806.04411","repositories_listed":1,"syntology":null},{"url":"/paper/learning-joint-embedding-with-multimodal-cues","slug":"learning-joint-embedding-with-multimodal-cues","title":"Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval","date":"2018-06-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-search-in-long-documents-using","slug":"learning-to-search-in-long-documents-using","title":"Learning to Search in Long Documents Using Document Structure","date":"2018-06-09","arxiv_id":"1806.03529","repositories_listed":1,"syntology":null},{"url":"/paper/deepfirearm-learning-discriminative-feature","slug":"deepfirearm-learning-discriminative-feature","title":"DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval","date":"2018-06-08","arxiv_id":"1806.02984","repositories_listed":1,"syntology":null},{"url":"/paper/jtav-jointly-learning-social-media-content","slug":"jtav-jointly-learning-social-media-content","title":"JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features","date":"2018-06-05","arxiv_id":"1806.01483","repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-and-customizable-misspelling","slug":"an-unsupervised-and-customizable-misspelling","title":"An unsupervised and customizable misspelling generator for mining noisy health-related text sources","date":"2018-06-04","arxiv_id":"1806.00910","repositories_listed":1,"syntology":null},{"url":"/paper/deep-cauchy-hashing-for-hamming-space","slug":"deep-cauchy-hashing-for-hamming-space","title":"Deep Cauchy Hashing for Hamming Space Retrieval","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lamv-learning-to-align-and-match-videos-with","slug":"lamv-learning-to-align-and-match-videos-with","title":"LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/image-to-image-translation-for-cross-domain","slug":"image-to-image-translation-for-cross-domain","title":"Image-to-image translation for cross-domain disentanglement","date":"2018-05-24","arxiv_id":"1805.09730","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/image-to-image-translation-for-cross-domain#ran","syntology_url":"https://syntology.ai/paper/1805.09730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09730"}},"official":{"repos":["agonzgarc/cross-domain-disen"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/deep-feature-aggregation-and-image-re-ranking","slug":"deep-feature-aggregation-and-image-re-ranking","title":"Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval","date":"2018-05-22","arxiv_id":"1805.08587","repositories_listed":1,"syntology":null},{"url":"/paper/entity-duet-neural-ranking-understanding-the-1","slug":"entity-duet-neural-ranking-understanding-the-1","title":"Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval","date":"2018-05-19","arxiv_id":"1805.07591","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-hand-gesture-recognition-on","slug":"deep-learning-for-hand-gesture-recognition-on","title":"Deep Learning for Hand Gesture Recognition on Skeletal Data","date":"2018-05-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-cluster-unary-loss-for-efficient","slug":"semantic-cluster-unary-loss-for-efficient","title":"Semantic Cluster Unary Loss for Efficient Deep Hashing","date":"2018-05-15","arxiv_id":"1805.08705","repositories_listed":1,"syntology":null},{"url":"/paper/nash-toward-end-to-end-neural-architecture","slug":"nash-toward-end-to-end-neural-architecture","title":"NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing","date":"2018-05-14","arxiv_id":"1805.05361","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-document-retrieval-using","slug":"cross-lingual-document-retrieval-using","title":"Cross-lingual Document Retrieval using Regularized Wasserstein Distance","date":"2018-05-11","arxiv_id":"1805.04437","repositories_listed":1,"syntology":null},{"url":"/paper/polite-dialogue-generation-without-parallel","slug":"polite-dialogue-generation-without-parallel","title":"Polite Dialogue Generation Without Parallel Data","date":"2018-05-08","arxiv_id":"1805.03162","repositories_listed":1,"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/polite-dialogue-generation-without-parallel#ran","syntology_url":"https://syntology.ai/paper/1805.03162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.03162"}},"official":null}},{"url":"/paper/mtfh-a-matrix-tri-factorization-hashing","slug":"mtfh-a-matrix-tri-factorization-hashing","title":"MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval","date":"2018-05-04","arxiv_id":"1805.01963","repositories_listed":1,"syntology":null},{"url":"/paper/images-recipes-retrieval-in-the-cooking","slug":"images-recipes-retrieval-in-the-cooking","title":"Images & Recipes: Retrieval in the cooking context","date":"2018-05-02","arxiv_id":"1805.00900","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-pins-cross-modal-embeddings-for","slug":"learnable-pins-cross-modal-embeddings-for","title":"Learnable PINs: Cross-Modal Embeddings for Person Identity","date":"2018-05-02","arxiv_id":"1805.00833","repositories_listed":1,"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":3,"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/learnable-pins-cross-modal-embeddings-for#ran","syntology_url":"https://syntology.ai/paper/1805.00833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.00833"}},"official":null}},{"url":"/paper/unsupervised-cross-lingual-information","slug":"unsupervised-cross-lingual-information","title":"Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only","date":"2018-05-02","arxiv_id":"1805.00879","repositories_listed":1,"syntology":null},{"url":"/paper/dialog-based-interactive-image-retrieval","slug":"dialog-based-interactive-image-retrieval","title":"Dialog-based Interactive Image Retrieval","date":"2018-05-01","arxiv_id":"1805.00145","repositories_listed":1,"syntology":null},{"url":"/paper/response-ranking-with-deep-matching-networks","slug":"response-ranking-with-deep-matching-networks","title":"Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems","date":"2018-05-01","arxiv_id":"1805.00188","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-retrieval-in-the-cooking-context","slug":"cross-modal-retrieval-in-the-cooking-context","title":"Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings","date":"2018-04-30","arxiv_id":"1804.11146","repositories_listed":1,"syntology":null},{"url":"/paper/how-convolutional-neural-network-see-the","slug":"how-convolutional-neural-network-see-the","title":"How convolutional neural network see the world - A survey of convolutional neural network visualization methods","date":"2018-04-30","arxiv_id":"1804.11191","repositories_listed":1,"syntology":null},{"url":"/paper/cross-media-multi-level-alignment-with","slug":"cross-media-multi-level-alignment-with","title":"Cross-media Multi-level Alignment with Relation Attention Network","date":"2018-04-25","arxiv_id":"1804.09539","repositories_listed":1,"syntology":null},{"url":"/paper/phrase-indexed-question-answering-a-new","slug":"phrase-indexed-question-answering-a-new","title":"Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension","date":"2018-04-20","arxiv_id":"1804.07726","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/phrase-indexed-question-answering-a-new#ran","syntology_url":"https://syntology.ai/paper/1804.07726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07726"}},"official":{"repos":["uwnlp/piqa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-disentangled-representations-of","slug":"learning-disentangled-representations-of","title":"Learning Disentangled Representations of Texts with Application to Biomedical Abstracts","date":"2018-04-19","arxiv_id":"1804.07212","repositories_listed":1,"syntology":null},{"url":"/paper/community-member-retrieval-on-social-media","slug":"community-member-retrieval-on-social-media","title":"Community Member Retrieval on Social Media using Textual Information","date":"2018-04-16","arxiv_id":"1804.05499","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-cross-modality-retrieval-with-cca","slug":"end-to-end-cross-modality-retrieval-with-cca","title":"End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss","date":"2018-04-16","arxiv_id":"1705.06979","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-deep-listwise-context-model-for","slug":"learning-a-deep-listwise-context-model-for","title":"Learning a Deep Listwise Context Model for Ranking Refinement","date":"2018-04-16","arxiv_id":"1804.05936","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/learning-a-deep-listwise-context-model-for#ran","syntology_url":"https://syntology.ai/paper/1804.05936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05936"}},"official":{"repos":["QingyaoAi/Deep-Listwise-Context-Model-for-Ranking-Refinement"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/improving-the-representation-and-conversion","slug":"improving-the-representation-and-conversion","title":"Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context","date":"2018-04-13","arxiv_id":"1804.04956","repositories_listed":1,"syntology":null},{"url":"/paper/multilevel-language-and-vision-integration","slug":"multilevel-language-and-vision-integration","title":"Multilevel Language and Vision Integration for Text-to-Clip Retrieval","date":"2018-04-13","arxiv_id":"1804.05113","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multilevel-language-and-vision-integration#ran","syntology_url":"https://syntology.ai/paper/1804.05113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05113"}},"official":{"repos":["VisionLearningGroup/Text-to-Clip_Retrieval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-autoencoding-models-for-unsupervised","slug":"deep-autoencoding-models-for-unsupervised","title":"Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images","date":"2018-04-12","arxiv_id":"1804.04488","repositories_listed":1,"syntology":null},{"url":"/paper/pix3d-dataset-and-methods-for-single-image-3d","slug":"pix3d-dataset-and-methods-for-single-image-3d","title":"Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling","date":"2018-04-12","arxiv_id":"1804.04610","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-image-matching-with-deep-feature","slug":"cross-domain-image-matching-with-deep-feature","title":"Cross-Domain Image Matching with Deep Feature Maps","date":"2018-04-06","arxiv_id":"1804.02367","repositories_listed":1,"syntology":null},{"url":"/paper/finding-beans-in-burgers-deep-semantic-visual","slug":"finding-beans-in-burgers-deep-semantic-visual","title":"Finding beans in burgers: Deep semantic-visual embedding with localization","date":"2018-04-05","arxiv_id":"1804.01720","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-adversarial-hashing-networks","slug":"self-supervised-adversarial-hashing-networks","title":"Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval","date":"2018-04-04","arxiv_id":"1804.01223","repositories_listed":1,"syntology":null},{"url":"/paper/sketchmate-deep-hashing-for-million-scale","slug":"sketchmate-deep-hashing-for-million-scale","title":"SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval","date":"2018-04-04","arxiv_id":"1804.01401","repositories_listed":1,"syntology":null},{"url":"/paper/design-design-inspiration-from-generative","slug":"design-design-inspiration-from-generative","title":"DeSIGN: Design Inspiration from Generative Networks","date":"2018-04-03","arxiv_id":"1804.00921","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/design-design-inspiration-from-generative#ran","syntology_url":"https://syntology.ai/paper/1804.00921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00921"}},"official":null}},{"url":"/paper/unsupervised-semantic-based-aggregation-of","slug":"unsupervised-semantic-based-aggregation-of","title":"Unsupervised Semantic-based Aggregation of Deep Convolutional Features","date":"2018-04-03","arxiv_id":"1804.01422","repositories_listed":1,"syntology":null},{"url":"/paper/mining-on-manifolds-metric-learning-without","slug":"mining-on-manifolds-metric-learning-without","title":"Mining on Manifolds: Metric Learning without Labels","date":"2018-03-29","arxiv_id":"1803.11095","repositories_listed":1,"syntology":null},{"url":"/paper/inloc-indoor-visual-localization-with-dense","slug":"inloc-indoor-visual-localization-with-dense","title":"InLoc: Indoor Visual Localization with Dense Matching and View Synthesis","date":"2018-03-28","arxiv_id":"1803.10368","repositories_listed":1,"syntology":null},{"url":"/paper/web2text-deep-structured-boilerplate-removal","slug":"web2text-deep-structured-boilerplate-removal","title":"Web2Text: Deep Structured Boilerplate Removal","date":"2018-03-27","arxiv_id":"1801.02607","repositories_listed":1,"syntology":null},{"url":"/paper/staqc-a-systematically-mined-question-code","slug":"staqc-a-systematically-mined-question-code","title":"StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow","date":"2018-03-26","arxiv_id":"1803.09371","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-how-developers-use-general-purpose","slug":"evaluating-how-developers-use-general-purpose","title":"Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval","date":"2018-03-22","arxiv_id":"1803.08612","repositories_listed":1,"syntology":null},{"url":"/paper/object-captioning-and-retrieval-with-natural","slug":"object-captioning-and-retrieval-with-natural","title":"Object Captioning and Retrieval with Natural Language","date":"2018-03-16","arxiv_id":"1803.06152","repositories_listed":1,"syntology":null},{"url":"/paper/triplet-center-loss-for-multi-view-3d-object","slug":"triplet-center-loss-for-multi-view-3d-object","title":"Triplet-Center Loss for Multi-View 3D Object Retrieval","date":"2018-03-16","arxiv_id":"1803.06189","repositories_listed":1,"syntology":null},{"url":"/paper/think-you-have-solved-question-answering-try","slug":"think-you-have-solved-question-answering-try","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","date":"2018-03-14","arxiv_id":"1803.05457","repositories_listed":1,"syntology":null},{"url":"/paper/deep-class-wise-hashing-semantics-preserving","slug":"deep-class-wise-hashing-semantics-preserving","title":"Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss","date":"2018-03-12","arxiv_id":"1803.04137","repositories_listed":1,"syntology":null},{"url":"/paper/generalization-in-metric-learning-should-the","slug":"generalization-in-metric-learning-should-the","title":"Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?","date":"2018-03-08","arxiv_id":"1803.03310","repositories_listed":1,"syntology":null},{"url":"/paper/instance-similarity-deep-hashing-for-multi","slug":"instance-similarity-deep-hashing-for-multi","title":"Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval","date":"2018-03-08","arxiv_id":"1803.02987","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-unlabeled-data-for-crowd-counting","slug":"leveraging-unlabeled-data-for-crowd-counting","title":"Leveraging Unlabeled Data for Crowd Counting by Learning to Rank","date":"2018-03-08","arxiv_id":"1803.03095","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-sketch-image-hashing","slug":"zero-shot-sketch-image-hashing","title":"Zero-Shot Sketch-Image Hashing","date":"2018-03-06","arxiv_id":"1803.02284","repositories_listed":1,"syntology":null},{"url":"/paper/authorship-verification-in-the-absence-of","slug":"authorship-verification-in-the-absence-of","title":"Authorship verification in the absence of explicit features and thresholds","date":"2018-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/directional-statistics-based-deep-metric","slug":"directional-statistics-based-deep-metric","title":"Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval","date":"2018-02-27","arxiv_id":"1802.09662","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/directional-statistics-based-deep-metric#ran","syntology_url":"https://syntology.ai/paper/1802.09662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09662"}},"official":null}},{"url":"/paper/hbst-a-hamming-distance-embedding-binary","slug":"hbst-a-hamming-distance-embedding-binary","title":"HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition","date":"2018-02-26","arxiv_id":"1802.09261","repositories_listed":1,"syntology":null},{"url":"/paper/inverting-the-generator-of-a-generative-1","slug":"inverting-the-generator-of-a-generative-1","title":"Inverting The Generator Of A Generative Adversarial Network (II)","date":"2018-02-15","arxiv_id":"1802.05701","repositories_listed":1,"syntology":null},{"url":"/paper/one-deep-music-representation-to-rule-them","slug":"one-deep-music-representation-to-rule-them","title":"One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies","date":"2018-02-12","arxiv_id":"1802.04051","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-vector-space-model-sparse","slug":"revisiting-the-vector-space-model-sparse","title":"Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification","date":"2018-02-12","arxiv_id":"1802.03938","repositories_listed":1,"syntology":null},{"url":"/paper/from-selective-deep-convolutional-features-to","slug":"from-selective-deep-convolutional-features-to","title":"From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval","date":"2018-02-07","arxiv_id":"1802.02899","repositories_listed":1,"syntology":null},{"url":"/paper/holoface-augmenting-human-to-human","slug":"holoface-augmenting-human-to-human","title":"HoloFace: Augmenting Human-to-Human Interactions on HoloLens","date":"2018-02-01","arxiv_id":"1802.00278","repositories_listed":1,"syntology":null},{"url":"/paper/ternarynet-faster-deep-model-inference","slug":"ternarynet-faster-deep-model-inference","title":"TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions","date":"2018-01-29","arxiv_id":"1801.09449","repositories_listed":1,"syntology":null},{"url":"/paper/object-category-learning-and-retrieval-with","slug":"object-category-learning-and-retrieval-with","title":"Object category learning and retrieval with weak supervision","date":"2018-01-26","arxiv_id":"1801.08985","repositories_listed":1,"syntology":null},{"url":"/paper/dual-asymmetric-deep-hashing-learning","slug":"dual-asymmetric-deep-hashing-learning","title":"Dual Asymmetric Deep Hashing Learning","date":"2018-01-25","arxiv_id":"1801.08360","repositories_listed":1,"syntology":null},{"url":"/paper/sentipers-a-sentiment-analysis-corpus-for","slug":"sentipers-a-sentiment-analysis-corpus-for","title":"SentiPers: A Sentiment Analysis Corpus for Persian","date":"2018-01-23","arxiv_id":"1801.07737","repositories_listed":1,"syntology":null},{"url":"/paper/a-resource-light-method-for-cross-lingual","slug":"a-resource-light-method-for-cross-lingual","title":"A Resource-Light Method for Cross-Lingual Semantic Textual Similarity","date":"2018-01-19","arxiv_id":"1801.06436","repositories_listed":1,"syntology":null}],"record_sha256":"cb6efb6c09da38c28cbd5c0a821dbf4f42dc2860fb4f8de657efe995146ff74b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}