{"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/52","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":52,"pages_in_order":143,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/task/retrieval/papers/53","papers":[{"url":"/paper/deep-metric-learning-with-bier-boosting","slug":"deep-metric-learning-with-bier-boosting","title":"Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly","date":"2018-01-15","arxiv_id":"1801.04815","repositories_listed":1,"syntology":null},{"url":"/paper/deep-episodic-memory-encoding-recalling-and","slug":"deep-episodic-memory-encoding-recalling-and","title":"Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution","date":"2018-01-12","arxiv_id":"1801.04134","repositories_listed":1,"syntology":null},{"url":"/paper/sketchygan-towards-diverse-and-realistic","slug":"sketchygan-towards-diverse-and-realistic","title":"SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis","date":"2018-01-09","arxiv_id":"1801.02753","repositories_listed":1,"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/sketchygan-towards-diverse-and-realistic#ran","syntology_url":"https://syntology.ai/paper/1801.02753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.02753"}},"official":{"repos":["wchen342/SketchyGAN"],"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/cross-modal-embeddings-for-video-and-audio","slug":"cross-modal-embeddings-for-video-and-audio","title":"Cross-modal Embeddings for Video and Audio Retrieval","date":"2018-01-07","arxiv_id":"1801.02200","repositories_listed":1,"syntology":null},{"url":"/paper/a-large-dataset-for-improving-patch-matching","slug":"a-large-dataset-for-improving-patch-matching","title":"A Large Dataset for Improving Patch Matching","date":"2018-01-04","arxiv_id":"1801.01466","repositories_listed":1,"syntology":null},{"url":"/paper/deep-hashing-with-category-mask-for-fast","slug":"deep-hashing-with-category-mask-for-fast","title":"Deep Hashing with Category Mask for Fast Video Retrieval","date":"2017-12-22","arxiv_id":"1712.08315","repositories_listed":1,"syntology":null},{"url":"/paper/use-of-deep-learning-in-modern-recommendation","slug":"use-of-deep-learning-in-modern-recommendation","title":"Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works","date":"2017-12-20","arxiv_id":"1712.07525","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-memory-networks-efficient-machine","slug":"attentive-memory-networks-efficient-machine","title":"Attentive Memory Networks: Efficient Machine Reading for Conversational Search","date":"2017-12-19","arxiv_id":"1712.07229","repositories_listed":1,"syntology":null},{"url":"/paper/image-super-resolution-via-feature-augmented","slug":"image-super-resolution-via-feature-augmented","title":"Image Super-resolution via Feature-augmented Random Forest","date":"2017-12-14","arxiv_id":"1712.05248","repositories_listed":1,"syntology":null},{"url":"/paper/relation-extraction-a-survey","slug":"relation-extraction-a-survey","title":"Relation Extraction : A Survey","date":"2017-12-14","arxiv_id":"1712.05191","repositories_listed":1,"syntology":null},{"url":"/paper/o-cnn-octree-based-convolutional-neural","slug":"o-cnn-octree-based-convolutional-neural","title":"O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis","date":"2017-12-05","arxiv_id":"1712.01537","repositories_listed":1,"syntology":null},{"url":"/paper/sketching-out-the-details-sketch-based-image","slug":"sketching-out-the-details-sketch-based-image","title":"Sketching out the Details: Sketch-based Image Retrieval using Convolutional Neural Networks with Multi-stage Regression","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/saliency-weighted-convolutional-features-for","slug":"saliency-weighted-convolutional-features-for","title":"Saliency Weighted Convolutional Features for Instance Search","date":"2017-11-29","arxiv_id":"1711.10795","repositories_listed":1,"syntology":null},{"url":"/paper/separating-self-expression-and-visual-content","slug":"separating-self-expression-and-visual-content","title":"Separating Self-Expression and Visual Content in Hashtag Supervision","date":"2017-11-27","arxiv_id":"1711.09825","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-speed-and-quality-in-online","slug":"balancing-speed-and-quality-in-online","title":"Balancing Speed and Quality in Online Learning to Rank for Information Retrieval","date":"2017-11-26","arxiv_id":"1711.09446","repositories_listed":1,"syntology":null},{"url":"/paper/rdf2vec-rdf-graph-embeddings-and-their","slug":"rdf2vec-rdf-graph-embeddings-and-their","title":"RDF2Vec: RDF Graph Embeddings and Their Applications","date":"2017-11-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automatic-query-image-disambiguation-for","slug":"automatic-query-image-disambiguation-for","title":"Automatic Query Image Disambiguation for Content-Based Image Retrieval","date":"2017-11-02","arxiv_id":"1711.00953","repositories_listed":1,"syntology":null},{"url":"/paper/optimization-of-phase-retrieval-in-the","slug":"optimization-of-phase-retrieval-in-the","title":"Optimization of phase retrieval in the Fresnel domain by the modified Gerchberg-Saxton algorithm","date":"2017-11-02","arxiv_id":"1711.01176","repositories_listed":1,"syntology":null},{"url":"/paper/open-set-logo-detection-and-retrieval","slug":"open-set-logo-detection-and-retrieval","title":"Open Set Logo Detection and Retrieval","date":"2017-10-30","arxiv_id":"1710.10891","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/open-set-logo-detection-and-retrieval#ran","syntology_url":"https://syntology.ai/paper/1710.10891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.10891"}},"official":null}},{"url":"/paper/complete-3d-scene-parsing-from-an-rgbd-image","slug":"complete-3d-scene-parsing-from-an-rgbd-image","title":"Complete 3D Scene Parsing from an RGBD Image","date":"2017-10-25","arxiv_id":"1710.09490","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-event-continuous-time-rnns","slug":"discrete-event-continuous-time-rnns","title":"Discrete Event, Continuous Time RNNs","date":"2017-10-11","arxiv_id":"1710.04110","repositories_listed":1,"syntology":null},{"url":"/paper/corner-based-geometric-calibration-of-multi","slug":"corner-based-geometric-calibration-of-multi","title":"Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras","date":"2017-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/are-we-done-with-object-recognition-the-icub","slug":"are-we-done-with-object-recognition-the-icub","title":"Are we done with object recognition? The iCub robot's perspective","date":"2017-09-28","arxiv_id":"1709.09882","repositories_listed":1,"syntology":null},{"url":"/paper/challenging-neural-dialogue-models-with","slug":"challenging-neural-dialogue-models-with","title":"Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena","date":"2017-09-22","arxiv_id":"1709.07840","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-generative-framework-for-paraphrase","slug":"a-deep-generative-framework-for-paraphrase","title":"A Deep Generative Framework for Paraphrase Generation","date":"2017-09-15","arxiv_id":"1709.05074","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-preserving-embeddings-for","slug":"semantic-preserving-embeddings-for","title":"Semantic Preserving Embeddings for Generalized Graphs","date":"2017-09-07","arxiv_id":"1709.02759","repositories_listed":1,"syntology":null},{"url":"/paper/cross-media-similarity-evaluation-for-web","slug":"cross-media-similarity-evaluation-for-web","title":"Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild","date":"2017-09-05","arxiv_id":"1709.01305","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-visual-features-from-text-for","slug":"predicting-visual-features-from-text-for","title":"Predicting Visual Features from Text for Image and Video Caption Retrieval","date":"2017-09-05","arxiv_id":"1709.01362","repositories_listed":1,"syntology":null},{"url":"/paper/sketchparse-towards-rich-descriptions-for","slug":"sketchparse-towards-rich-descriptions-for","title":"SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks","date":"2017-09-05","arxiv_id":"1709.01295","repositories_listed":1,"syntology":null},{"url":"/paper/r3-reinforced-reader-ranker-for-open-domain","slug":"r3-reinforced-reader-ranker-for-open-domain","title":"R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering","date":"2017-08-31","arxiv_id":"1709.00023","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-measures-for-relevance-and","slug":"evaluation-measures-for-relevance-and","title":"Evaluation Measures for Relevance and Credibility in Ranked Lists","date":"2017-08-23","arxiv_id":"1708.07157","repositories_listed":1,"syntology":null},{"url":"/paper/deep-binary-reconstruction-for-cross-modal","slug":"deep-binary-reconstruction-for-cross-modal","title":"Deep Binary Reconstruction for Cross-modal Hashing","date":"2017-08-17","arxiv_id":"1708.05127","repositories_listed":1,"syntology":null},{"url":"/paper/modality-specific-cross-modal-similarity","slug":"modality-specific-cross-modal-similarity","title":"Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network","date":"2017-08-16","arxiv_id":"1708.04776","repositories_listed":1,"syntology":null},{"url":"/paper/simple-and-effective-dimensionality-reduction","slug":"simple-and-effective-dimensionality-reduction","title":"Simple and Effective Dimensionality Reduction for Word Embeddings","date":"2017-08-11","arxiv_id":"1708.03629","repositories_listed":1,"syntology":null},{"url":"/paper/binary-generative-adversarial-networks-for","slug":"binary-generative-adversarial-networks-for","title":"Binary Generative Adversarial Networks for Image Retrieval","date":"2017-08-08","arxiv_id":"1708.04150","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-repression-network-for-precise","slug":"learning-a-repression-network-for-precise","title":"Learning a Repression Network for Precise Vehicle Search","date":"2017-08-08","arxiv_id":"1708.02386","repositories_listed":1,"syntology":null},{"url":"/paper/learning-visual-importance-for-graphic","slug":"learning-visual-importance-for-graphic","title":"Learning Visual Importance for Graphic Designs and Data Visualizations","date":"2017-08-08","arxiv_id":"1708.02660","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-spatially-aware-fashion-concept","slug":"automatic-spatially-aware-fashion-concept","title":"Automatic Spatially-aware Fashion Concept Discovery","date":"2017-08-03","arxiv_id":"1708.01311","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-event-centric-document-collections","slug":"extracting-event-centric-document-collections","title":"Extracting Event-Centric Document Collections from Large-Scale Web Archives","date":"2017-07-28","arxiv_id":"1707.09217","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-representation-learning-for-fast","slug":"hyperbolic-representation-learning-for-fast","title":"Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering","date":"2017-07-25","arxiv_id":"1707.07847","repositories_listed":1,"syntology":null},{"url":"/paper/structural-regularities-in-text-based-entity","slug":"structural-regularities-in-text-based-entity","title":"Structural Regularities in Text-based Entity Vector Spaces","date":"2017-07-25","arxiv_id":"1707.07930","repositories_listed":1,"syntology":null},{"url":"/paper/matchzoo-a-toolkit-for-deep-text-matching","slug":"matchzoo-a-toolkit-for-deep-text-matching","title":"MatchZoo: A Toolkit for Deep Text Matching","date":"2017-07-23","arxiv_id":"1707.07270","repositories_listed":1,"syntology":null},{"url":"/paper/mag-a-multilingual-knowledge-base-agnostic","slug":"mag-a-multilingual-knowledge-base-agnostic","title":"MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach","date":"2017-07-17","arxiv_id":"1707.05288","repositories_listed":1,"syntology":null},{"url":"/paper/lyrics-based-music-genre-classification-using","slug":"lyrics-based-music-genre-classification-using","title":"Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network","date":"2017-07-15","arxiv_id":"1707.04678","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-manifold-embedding-layer-learned-by","slug":"iterative-manifold-embedding-layer-learned-by","title":"Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval","date":"2017-07-14","arxiv_id":"1707.09862","repositories_listed":1,"syntology":null},{"url":"/paper/quasar-datasets-for-question-answering-by","slug":"quasar-datasets-for-question-answering-by","title":"Quasar: Datasets for Question Answering by Search and Reading","date":"2017-07-12","arxiv_id":"1707.03904","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/quasar-datasets-for-question-answering-by#ran","syntology_url":"https://syntology.ai/paper/1707.03904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.03904"}},"official":{"repos":["bdhingra/quasar"],"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/deep-discrete-hashing-with-self-supervised","slug":"deep-discrete-hashing-with-self-supervised","title":"Deep Discrete Hashing with Self-supervised Pairwise Labels","date":"2017-07-07","arxiv_id":"1707.02112","repositories_listed":1,"syntology":null},{"url":"/paper/selective-deep-convolutional-features-for","slug":"selective-deep-convolutional-features-for","title":"Selective Deep Convolutional Features for Image Retrieval","date":"2017-07-04","arxiv_id":"1707.00809","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-submodular-rank-aggregation-on","slug":"unsupervised-submodular-rank-aggregation-on","title":"Unsupervised Submodular Rank Aggregation on Score-based Permutations","date":"2017-07-04","arxiv_id":"1707.01166","repositories_listed":1,"syntology":null},{"url":"/paper/pedestrian-alignment-network-for-large-scale","slug":"pedestrian-alignment-network-for-large-scale","title":"Pedestrian Alignment Network for Large-scale Person Re-identification","date":"2017-07-03","arxiv_id":"1707.00408","repositories_listed":1,"syntology":null},{"url":"/paper/comparative-evaluation-of-hand-crafted-and-1","slug":"comparative-evaluation-of-hand-crafted-and-1","title":"Comparative Evaluation of Hand-Crafted and Learned Local Features","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/path-planning-for-robotic-mobile-fulfillment","slug":"path-planning-for-robotic-mobile-fulfillment","title":"Path planning for Robotic Mobile Fulfillment Systems","date":"2017-06-28","arxiv_id":"1706.09347","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-entity-retrieval-toolkit","slug":"semantic-entity-retrieval-toolkit","title":"Semantic Entity Retrieval Toolkit","date":"2017-06-12","arxiv_id":"1706.03757","repositories_listed":1,"syntology":null},{"url":"/paper/see-hear-and-read-deep-aligned","slug":"see-hear-and-read-deep-aligned","title":"See, Hear, and Read: Deep Aligned Representations","date":"2017-06-03","arxiv_id":"1706.00932","repositories_listed":1,"syntology":null},{"url":"/paper/deep-image-representations-using-caption","slug":"deep-image-representations-using-caption","title":"Deep image representations using caption generators","date":"2017-05-25","arxiv_id":"1705.09142","repositories_listed":1,"syntology":null},{"url":"/paper/how-a-general-purpose-commonsense-ontology","slug":"how-a-general-purpose-commonsense-ontology","title":"How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval","date":"2017-05-24","arxiv_id":"1705.08844","repositories_listed":1,"syntology":null},{"url":"/paper/hashing-as-tie-aware-learning-to-rank","slug":"hashing-as-tie-aware-learning-to-rank","title":"Hashing as Tie-Aware Learning to Rank","date":"2017-05-23","arxiv_id":"1705.08562","repositories_listed":1,"syntology":null},{"url":"/paper/sample-efficient-algorithms-for-recovering","slug":"sample-efficient-algorithms-for-recovering","title":"Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements","date":"2017-05-18","arxiv_id":"1705.06412","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-hierarchical-latent-variable-model","slug":"learning-a-hierarchical-latent-variable-model","title":"Learning a Hierarchical Latent-Variable Model of 3D Shapes","date":"2017-05-17","arxiv_id":"1705.05994","repositories_listed":1,"syntology":null},{"url":"/paper/benchmark-for-complex-answer-retrieval-1","slug":"benchmark-for-complex-answer-retrieval-1","title":"Benchmark for Complex Answer Retrieval","date":"2017-05-13","arxiv_id":"1705.04803","repositories_listed":1,"syntology":null},{"url":"/paper/kate-k-competitive-autoencoder-for-text","slug":"kate-k-competitive-autoencoder-for-text","title":"KATE: K-Competitive Autoencoder for Text","date":"2017-05-04","arxiv_id":"1705.02033","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-part-based-weighting-aggregation","slug":"unsupervised-part-based-weighting-aggregation","title":"Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval","date":"2017-05-03","arxiv_id":"1705.01247","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-active-search-using-linear-similarity","slug":"scaling-active-search-using-linear-similarity","title":"Scaling Active Search using Linear Similarity Functions","date":"2017-04-30","arxiv_id":"1705.00334","repositories_listed":1,"syntology":null},{"url":"/paper/neural-ranking-models-with-weak-supervision","slug":"neural-ranking-models-with-weak-supervision","title":"Neural Ranking Models with Weak Supervision","date":"2017-04-28","arxiv_id":"1704.08803","repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-nearest-neighbor-search-with","slug":"accelerated-nearest-neighbor-search-with","title":"Accelerated Nearest Neighbor Search with Quick ADC","date":"2017-04-24","arxiv_id":"1704.07355","repositories_listed":1,"syntology":null},{"url":"/paper/convex-formulation-of-multiple-instance","slug":"convex-formulation-of-multiple-instance","title":"Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags","date":"2017-04-22","arxiv_id":"1704.06767","repositories_listed":1,"syntology":null},{"url":"/paper/answering-complex-questions-using-open","slug":"answering-complex-questions-using-open","title":"Answering Complex Questions Using Open Information Extraction","date":"2017-04-19","arxiv_id":"1704.05572","repositories_listed":1,"syntology":null},{"url":"/paper/learning-two-branch-neural-networks-for-image","slug":"learning-two-branch-neural-networks-for-image","title":"Learning Two-Branch Neural Networks for Image-Text Matching Tasks","date":"2017-04-11","arxiv_id":"1704.03470","repositories_listed":1,"syntology":null},{"url":"/paper/utterance-retrieval-based-on-recurrent","slug":"utterance-retrieval-based-on-recurrent","title":"Utterance Retrieval Based on Recurrent Surface Text Patterns","date":"2017-04-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-search-for-structured-data-via","slug":"probabilistic-search-for-structured-data-via","title":"Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes","date":"2017-04-04","arxiv_id":"1704.01087","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-information-retrieval-for","slug":"discriminative-information-retrieval-for","title":"Discriminative Information Retrieval for Question Answering Sentence Selection","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/which-is-the-effective-way-for-gaokao","slug":"which-is-the-effective-way-for-gaokao","title":"Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mihash-online-hashing-with-mutual-information","slug":"mihash-online-hashing-with-mutual-information","title":"MIHash: Online Hashing with Mutual Information","date":"2017-03-27","arxiv_id":"1703.08919","repositories_listed":1,"syntology":null},{"url":"/paper/medical-image-retrieval-using-deep","slug":"medical-image-retrieval-using-deep","title":"Medical Image Retrieval using Deep Convolutional Neural Network","date":"2017-03-24","arxiv_id":"1703.08472","repositories_listed":1,"syntology":null},{"url":"/paper/fast-spectral-ranking-for-similarity-search","slug":"fast-spectral-ranking-for-similarity-search","title":"Fast Spectral Ranking for Similarity Search","date":"2017-03-20","arxiv_id":"1703.06935","repositories_listed":1,"syntology":null},{"url":"/paper/deep-sketch-hashing-fast-free-hand-sketch","slug":"deep-sketch-hashing-fast-free-hand-sketch","title":"Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval","date":"2017-03-16","arxiv_id":"1703.05605","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-of-neural-classifiers-for-scoring","slug":"ensemble-of-neural-classifiers-for-scoring","title":"Ensemble of Neural Classifiers for Scoring Knowledge Base Triples","date":"2017-03-15","arxiv_id":"1703.04914","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-query-image-representation-for","slug":"context-aware-query-image-representation-for","title":"Context Aware Query Image Representation for Particular Object Retrieval","date":"2017-03-03","arxiv_id":"1703.01226","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-nearest-neighbor","slug":"learning-deep-nearest-neighbor","title":"Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees","date":"2017-02-28","arxiv_id":"1702.08833","repositories_listed":1,"syntology":null},{"url":"/paper/luandri-a-clean-lua-interface-to-the-indri","slug":"luandri-a-clean-lua-interface-to-the-indri","title":"Luandri: a Clean Lua Interface to the Indri Search Engine","date":"2017-02-16","arxiv_id":"1702.05042","repositories_listed":1,"syntology":null},{"url":"/paper/scannet-richly-annotated-3d-reconstructions","slug":"scannet-richly-annotated-3d-reconstructions","title":"ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes","date":"2017-02-14","arxiv_id":"1702.04405","repositories_listed":1,"syntology":null},{"url":"/paper/geometric-features-for-voxel-based-surface","slug":"geometric-features-for-voxel-based-surface","title":"Geometric features for voxel-based surface recognition","date":"2017-01-16","arxiv_id":"1701.04249","repositories_listed":1,"syntology":null},{"url":"/paper/ruber-an-unsupervised-method-for-automatic","slug":"ruber-an-unsupervised-method-for-automatic","title":"RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems","date":"2017-01-11","arxiv_id":"1701.03079","repositories_listed":1,"syntology":null},{"url":"/paper/neuro-symbolic-representation-learning-on","slug":"neuro-symbolic-representation-learning-on","title":"Neuro-symbolic representation learning on biological knowledge graphs","date":"2016-12-13","arxiv_id":"1612.04256","repositories_listed":1,"syntology":null},{"url":"/paper/deep-supervised-hashing-with-triplet-labels","slug":"deep-supervised-hashing-with-triplet-labels","title":"Deep Supervised Hashing with Triplet Labels","date":"2016-12-12","arxiv_id":"1612.03900","repositories_listed":1,"syntology":null},{"url":"/paper/deep-metric-learning-via-facility-location","slug":"deep-metric-learning-via-facility-location","title":"Deep Metric Learning via Facility Location","date":"2016-12-05","arxiv_id":"1612.01213","repositories_listed":1,"syntology":null},{"url":"/paper/improved-deep-metric-learning-with-multi","slug":"improved-deep-metric-learning-with-multi","title":"Improved Deep Metric Learning with Multi-class N-pair Loss Objective","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fast-supervised-discrete-hashing-and-its","slug":"fast-supervised-discrete-hashing-and-its","title":"Fast Supervised Discrete Hashing and its Analysis","date":"2016-11-30","arxiv_id":"1611.10017","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-impact-of-entity-linking-in-microblog","slug":"on-the-impact-of-entity-linking-in-microblog","title":"On the Impact of Entity Linking in Microblog Real-Time Filtering","date":"2016-11-10","arxiv_id":"1611.03350","repositories_listed":1,"syntology":null},{"url":"/paper/what-is-the-best-practice-for-cnns-applied-to","slug":"what-is-the-best-practice-for-cnns-applied-to","title":"What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?","date":"2016-11-05","arxiv_id":"1611.01640","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-match-using-local-and-distributed-1","slug":"learning-to-match-using-local-and-distributed-1","title":"Learning to Match Using Local and Distributed Representations of Text for Web Search","date":"2016-10-26","arxiv_id":"1610.08136","repositories_listed":1,"syntology":null},{"url":"/paper/how-should-we-evaluate-supervised-hashing","slug":"how-should-we-evaluate-supervised-hashing","title":"How should we evaluate supervised hashing?","date":"2016-09-21","arxiv_id":"1609.06753","repositories_listed":1,"syntology":null},{"url":"/paper/towards-end-to-end-reinforcement-learning-of","slug":"towards-end-to-end-reinforcement-learning-of","title":"Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access","date":"2016-09-03","arxiv_id":"1609.00777","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-efficient-and-semantic-expertise","slug":"unsupervised-efficient-and-semantic-expertise","title":"Unsupervised, Efficient and Semantic Expertise Retrieval","date":"2016-08-23","arxiv_id":"1608.06651","repositories_listed":1,"syntology":null},{"url":"/paper/deepdiary-automatic-caption-generation-for","slug":"deepdiary-automatic-caption-generation-for","title":"DeepDiary: Automatic Caption Generation for Lifelogging Image Streams","date":"2016-08-12","arxiv_id":"1608.03819","repositories_listed":1,"syntology":null},{"url":"/paper/a-distance-for-hmms-based-on-aggregated","slug":"a-distance-for-hmms-based-on-aggregated","title":"A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration","date":"2016-08-05","arxiv_id":"1608.01747","repositories_listed":1,"syntology":null},{"url":"/paper/sift-meets-cnn-a-decade-survey-of-instance","slug":"sift-meets-cnn-a-decade-survey-of-instance","title":"SIFT Meets CNN: A Decade Survey of Instance Retrieval","date":"2016-08-05","arxiv_id":"1608.01807","repositories_listed":1,"syntology":null},{"url":"/paper/meta-a-unified-toolkit-for-text-retrieval-and","slug":"meta-a-unified-toolkit-for-text-retrieval-and","title":"MeTA: A Unified Toolkit for Text Retrieval and Analysis","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-metric-for-class-conditional-knn","slug":"learning-a-metric-for-class-conditional-knn","title":"Learning a metric for class-conditional KNN","date":"2016-07-11","arxiv_id":"1607.03050","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-deep-convolutional-neural-networks","slug":"explaining-deep-convolutional-neural-networks","title":"Explaining Deep Convolutional Neural Networks on Music Classification","date":"2016-07-08","arxiv_id":"1607.02444","repositories_listed":1,"syntology":null}],"record_sha256":"5569494315136a259fb81b118ac016b594b9ac832911399061abf8fb18028e3b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}