{"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/119","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":119,"pages_in_order":143,"rows_per_page":100,"rows":[11801,11900],"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/118","next":"/task/retrieval/papers/120","papers":[{"url":null,"slug":"joint-wasserstein-autoencoders-for-aligning","title":"Joint Wasserstein Autoencoders for Aligning Multimodal Embeddings","date":"2019-09-14","arxiv_id":"1909.06635","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-and-multi-source-transfers-in","title":"Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings","date":"2019-09-14","arxiv_id":"1909.06563","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-retrieval-using-untrained-neural","title":"Phase Retrieval using Untrained Neural Network Priors","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"precise-asymptotics-for-phase-retrieval-and","title":"Precise asymptotics for phase retrieval and compressed sensing with random generative priors","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieving-signals-with-deep-complex","title":"Retrieving Signals with Deep Complex Extractors","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"y-net-a-physics-constrained-and-semi","title":"Y-net: A Physics-constrained and Semi-supervised Learning Approach to the Phase Problem in Computational Electron Imaging","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ptychographic-phase-retrieval-by-proximal","title":"Ptychographic phase-retrieval by proximal algorithms","date":"2019-09-13","arxiv_id":"1909.06482","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendation-or-discrimination-quantifying","title":"Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems","date":"2019-09-13","arxiv_id":"1909.06429","repositories_listed":0,"syntology":null},{"url":null,"slug":"cvxnets-learnable-convex-decomposition","title":"CvxNet: Learnable Convex Decomposition","date":"2019-09-12","arxiv_id":"1909.05736","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-domain-portability-and","title":"Measuring Domain Portability and ErrorPropagation in Biomedical QA","date":"2019-09-12","arxiv_id":"1909.09704","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-aware-age-agnostic-visual-place","title":"Attention-Aware Age-Agnostic Visual Place Recognition","date":"2019-09-11","arxiv_id":"1909.05163","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-deep-neural-networks-possess-concept-space","title":"Do deep neural networks possess concept space grid cells?","date":"2019-09-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"report-on-the-8th-international-workshop-on","title":"Report on the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019)","date":"2019-09-11","arxiv_id":"1909.04954","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-hashing-learning-for-visual-and-semantic","title":"Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images","date":"2019-09-10","arxiv_id":"1909.04614","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-context-dependencies-in-multi-page","title":"A Study of Context Dependencies in Multi-page Product Search","date":"2019-09-09","arxiv_id":"1909.04031","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-retrieval-with-measurement-system","title":"Signal retrieval with measurement system knowledge using variational generative model","date":"2019-09-09","arxiv_id":"1909.04188","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-question-answering-using-tourism","title":"Large Scale Question Answering using Tourism Data","date":"2019-09-08","arxiv_id":"1909.03527","repositories_listed":0,"syntology":null},{"url":null,"slug":"mule-multimodal-universal-language-embedding","title":"MULE: Multimodal Universal Language Embedding","date":"2019-09-08","arxiv_id":"1909.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"affect-enriched-word-embeddings-for-news","title":"Affect Enriched Word Embeddings for News Information Retrieval","date":"2019-09-04","arxiv_id":"1909.01772","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-tag-based-font-retrieval-with","title":"Large-scale Tag-based Font Retrieval with Generative Feature Learning","date":"2019-09-04","arxiv_id":"1909.02072","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-regularization-by-denoising-with","title":"Online Regularization by Denoising with Applications to Phase Retrieval","date":"2019-09-04","arxiv_id":"1909.02040","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-pairwise-multi-perspective","title":"Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering","date":"2019-09-03","arxiv_id":"1909.01059","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-cross-modal-systems-leverage-semantic","title":"Do Cross Modal Systems Leverage Semantic Relationships?","date":"2019-09-03","arxiv_id":"1909.01976","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-salient-context-based-on-semantic","title":"Finding Salient Context based on Semantic Matching for Relevance Ranking","date":"2019-09-03","arxiv_id":"1909.01165","repositories_listed":0,"syntology":null},{"url":null,"slug":"polyresponse-a-rank-based-approach-to-task","title":"PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking","date":"2019-09-03","arxiv_id":"1909.01296","repositories_listed":0,"syntology":null},{"url":null,"slug":"know2look-commonsense-knowledge-for-visual-1","title":"Know2Look: Commonsense Knowledge for Visual Search","date":"2019-09-02","arxiv_id":"1909.00749","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-comparison-of-3d-correspondence","title":"Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds","date":"2019-09-02","arxiv_id":"1909.00866","repositories_listed":0,"syntology":null},{"url":null,"slug":"visir-visual-and-semantic-image-label","title":"VISIR: Visual and Semantic Image Label Refinement","date":"2019-09-02","arxiv_id":"1909.00741","repositories_listed":0,"syntology":null},{"url":null,"slug":"artpedia","title":"Artpedia","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-propbank-generation-for-turkish","title":"Automatic Propbank Generation for Turkish","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-question-answering-for-medical-mcqs","title":"Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-unsupervised-sentence-similarity","title":"Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-sentence-retrieval-from-comparable","title":"Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-textual-similarity-with-siamese","title":"Semantic Textual Similarity with Siamese Neural Networks","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"term-based-extraction-of-medical-information","title":"Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/fashion-retrieval-via-graph-reasoning","slug":"fashion-retrieval-via-graph-reasoning","title":"Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid","date":"2019-08-30","arxiv_id":"1908.11754","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-zero-attention-model-for-personalized","title":"A Zero Attention Model for Personalized Product Search","date":"2019-08-29","arxiv_id":"1908.11322","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-hashing-with-mixture-prior","title":"Document Hashing with Mixture-Prior Generative Models","date":"2019-08-29","arxiv_id":"1908.11078","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-representations-learned-by-multimodal","title":"Probing Representations Learned by Multimodal Recurrent and Transformer Models","date":"2019-08-29","arxiv_id":"1908.11125","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-retrieval-in-the-wild-through","title":"Texture Retrieval in the Wild through detection-based attributes","date":"2019-08-29","arxiv_id":"1908.11111","repositories_listed":0,"syntology":null},{"url":null,"slug":"explore-entity-embedding-effectiveness-in","title":"Explore Entity Embedding Effectiveness in Entity Retrieval","date":"2019-08-28","arxiv_id":"1908.10554","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-model-for-query-expansion-using","title":"A novel model for query expansion using pseudo-relevant web knowledge","date":"2019-08-27","arxiv_id":"1908.10193","repositories_listed":0,"syntology":null},{"url":null,"slug":"hrge-net-hierarchical-relational-graph","title":"HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition","date":"2019-08-27","arxiv_id":"1908.10098","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-world-conversational-ai-for-hotel","title":"Real-world Conversational AI for Hotel Bookings","date":"2019-08-27","arxiv_id":"1908.10001","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-measure-for-text-recognition","title":"End-To-End Measure for Text Recognition","date":"2019-08-26","arxiv_id":"1908.09584","repositories_listed":0,"syntology":null},{"url":"/paper/multi-granularity-representations-of-dialog","slug":"multi-granularity-representations-of-dialog","title":"Multi-Granularity Representations of Dialog","date":"2019-08-26","arxiv_id":"1908.09890","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-cnn-and-classic-features-for","title":"A Comparison of CNN and Classic Features for Image Retrieval","date":"2019-08-25","arxiv_id":"1908.09300","repositories_listed":0,"syntology":null},{"url":null,"slug":"blended-convolution-and-synthesis-for","title":"Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes","date":"2019-08-24","arxiv_id":"1908.10209","repositories_listed":0,"syntology":null},{"url":null,"slug":"lukthung-classification-using-neural-networks","title":"Lukthung Classification Using Neural Networks on Lyrics and Audios","date":"2019-08-23","arxiv_id":"1908.08769","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807673","title":"Learning Joint Embedding for Cross-Modal Retrieval","date":"2019-08-21","arxiv_id":"1908.07673","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807761","title":"Predict Emoji Combination with Retrieval Strategy","date":"2019-08-21","arxiv_id":"1908.07761","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807590","title":"From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories","date":"2019-08-20","arxiv_id":"1908.07590","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-effective-device-aware-federated","title":"Towards Effective Device-Aware Federated Learning","date":"2019-08-20","arxiv_id":"1908.07420","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-zero-shot-hashing","title":"Cross-modal Zero-shot Hashing","date":"2019-08-19","arxiv_id":"1908.07388","repositories_listed":0,"syntology":null},{"url":null,"slug":"genetic-algorithms-for-the-optimization-of","title":"Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval","date":"2019-08-19","arxiv_id":"1908.06896","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-disentanglement-for-generative","title":"Geometric Disentanglement for Generative Latent Shape Models","date":"2019-08-18","arxiv_id":"1908.06386","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-features-matter-effective-language","title":"Language Features Matter: Effective Language Representations for Vision-Language Tasks","date":"2019-08-17","arxiv_id":"1908.06327","repositories_listed":0,"syntology":null},{"url":null,"slug":"cfo-a-framework-for-building-production-nlp","title":"CFO: A Framework for Building Production NLP Systems","date":"2019-08-16","arxiv_id":"1908.06121","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-representations-and-agents-for","title":"Learning Representations and Agents for Information Retrieval","date":"2019-08-16","arxiv_id":"1908.06132","repositories_listed":0,"syntology":null},{"url":"/paper/unicoder-vl-a-universal-encoder-for-vision","slug":"unicoder-vl-a-universal-encoder-for-vision","title":"Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training","date":"2019-08-16","arxiv_id":"1908.06066","repositories_listed":0,"syntology":null},{"url":null,"slug":"deephums-deep-human-motion-signature-for-3d","title":"DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences","date":"2019-08-15","arxiv_id":"1908.05750","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamming-sentence-embeddings-for-information","title":"Hamming Sentence Embeddings for Information Retrieval","date":"2019-08-15","arxiv_id":"1908.05541","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-source-code-search-a-study-of-the","title":"Semantic Source Code Search: A Study of the Past and a Glimpse at the Future","date":"2019-08-15","arxiv_id":"1908.06738","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-human-pose-estimation-under-limited","title":"Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation","date":"2019-08-14","arxiv_id":"1908.05293","repositories_listed":0,"syntology":null},{"url":null,"slug":"linking-graph-entities-with-multiplicity-and","title":"Linking Graph Entities with Multiplicity and Provenance","date":"2019-08-13","arxiv_id":"1908.04464","repositories_listed":0,"syntology":null},{"url":null,"slug":"shrewd-semantic-hierarchy-based-relational","title":"SHREWD: Semantic Hierarchy-based Relational Embeddings for Weakly-supervised Deep Hashing","date":"2019-08-12","arxiv_id":"1908.05602","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-visual-embedding-for-cross-modal","title":"Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA","date":"2019-08-10","arxiv_id":"1908.03744","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-semantic-concept-expansion","title":"Knowledge Aware Semantic Concept Expansion for Image-Text Matching","date":"2019-08-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-action-retrieval-through","title":"Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings","date":"2019-08-09","arxiv_id":"1908.03477","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-phrase-localization-in-a","title":"Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework","date":"2019-08-08","arxiv_id":"1908.02950","repositories_listed":0,"syntology":null},{"url":null,"slug":"location-field-descriptors-single-image-3d","title":"Location Field Descriptors: Single Image 3D Model Retrieval in the Wild","date":"2019-08-07","arxiv_id":"1908.02853","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-mining-policy-classifying-forest-and","title":"Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval","date":"2019-08-07","arxiv_id":"1908.02425","repositories_listed":0,"syntology":null},{"url":null,"slug":"view-n-gram-network-for-3d-object-retrieval","title":"View N-gram Network for 3D Object Retrieval","date":"2019-08-06","arxiv_id":"1908.01958","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-content-based-image-retrieval-method","title":"A Fast Content-Based Image Retrieval Method Using Deep Visual Features","date":"2019-08-05","arxiv_id":"1908.01505","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-unified-embedding-for-visual","title":"Learning a Unified Embedding for Visual Search at Pinterest","date":"2019-08-05","arxiv_id":"1908.01707","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-versus-global-strategies-in-social","title":"Local versus Global Strategies in Social Query Expansion","date":"2019-08-05","arxiv_id":"1908.01868","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-context-retrieval-for-long-tail","title":"Unsupervised Context Retrieval for Long-tail Entities","date":"2019-08-05","arxiv_id":"1908.01798","repositories_listed":0,"syntology":null},{"url":null,"slug":"l2g-auto-encoder-understanding-point-clouds","title":"L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention","date":"2019-08-02","arxiv_id":"1908.00720","repositories_listed":0,"syntology":null},{"url":null,"slug":"arbengvec-arabic-english-cross-lingual-word","title":"ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ars_nitk-at-mediqa-2019analysing-various","title":"ARS\\_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-transfer-learning-and-domain-data","title":"Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grammatical-error-aware-incorrect-example","title":"Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iit-kgp-at-mediqa-2019-recognizing-question","title":"IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lasigebiotm-at-mediqa-2019-biomedical","title":"LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-representation-retrieval-on","title":"Lexical Representation \\& Retrieval on Monolingual Interpretative text production","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-document-representations-for-cross","title":"Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-for-domain-adaptation-in-arabic","title":"Segmentation for Domain Adaptation in Arabic","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-retrieval-based-dialogue-systems-a-short","title":"Deep Retrieval-Based Dialogue Systems: A Short Review","date":"2019-07-30","arxiv_id":"1907.12878","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-cross-modal-hashing-with-hashing","title":"Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning","date":"2019-07-29","arxiv_id":"1907.12490","repositories_listed":0,"syntology":null},{"url":null,"slug":"aiads-automated-and-intelligent-advertising","title":"AiAds: Automated and Intelligent Advertising System for Sponsored Search","date":"2019-07-28","arxiv_id":"1907.12118","repositories_listed":0,"syntology":null},{"url":null,"slug":"topicsifter-interactive-search-space","title":"TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modeling","date":"2019-07-28","arxiv_id":"1907.12079","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-benchmark-on-tricks-for-large-scale-image","title":"A Benchmark on Tricks for Large-scale Image Retrieval","date":"2019-07-27","arxiv_id":"1907.11854","repositories_listed":0,"syntology":null},{"url":null,"slug":"amplitude-retrieval-for-channel-estimation-of","title":"Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs","date":"2019-07-27","arxiv_id":"1907.11904","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-attention-based-decoupled-metric-1","title":"Hybrid-Attention based Decoupled Metric Learning for Zero-Shot Image Retrieval","date":"2019-07-27","arxiv_id":"1907.11832","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-synthesize-robust-phase-retrieval","title":"Learning to Synthesize: Robust Phase Retrieval at Low Photon counts","date":"2019-07-26","arxiv_id":"1907.11713","repositories_listed":0,"syntology":null},{"url":null,"slug":"composition-aware-image-aesthetics-assessment","title":"Composition-Aware Image Aesthetics Assessment","date":"2019-07-25","arxiv_id":"1907.10801","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-ranking-based-cost-sensitive-multi-label","title":"Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction","date":"2019-07-25","arxiv_id":"1907.11521","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-dynamic-interactions-between","title":"Modelling Dynamic Interactions between Relevance Dimensions","date":"2019-07-25","arxiv_id":"1907.10943","repositories_listed":0,"syntology":null},{"url":"/paper/careful-selection-of-knowledge-to-solve-open","slug":"careful-selection-of-knowledge-to-solve-open","title":"Careful Selection of Knowledge to solve Open Book Question Answering","date":"2019-07-24","arxiv_id":"1907.10738","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-sentence-functions-for-short","title":"Fine-Grained Sentence Functions for Short-Text Conversation","date":"2019-07-24","arxiv_id":"1907.10302","repositories_listed":0,"syntology":null}],"record_sha256":"900547beac4899b59fa963a4cc84de8cacf420a6396e1a4b9c00443b08ea8771","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}