{"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/109","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":109,"pages_in_order":143,"rows_per_page":100,"rows":[10801,10900],"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/108","next":"/task/retrieval/papers/110","papers":[{"url":null,"slug":"probabilistic-multimodal-representation","title":"Probabilistic Multimodal Representation Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-temporal-learning","title":"Self-supervised Temporal Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-hashing-with-locality-sensitive","title":"Semantic Hashing with Locality Sensitive Embeddings","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sketchaa-abstract-representation-for-abstract","title":"SketchAA: Abstract Representation for Abstract Sketches","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spectrally-similar-graph-pooling","title":"Spectrally Similar Graph Pooling","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-memory-guided-neural-machine","title":"Translation Memory Guided Neural Machine Translation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"umec-unified-model-and-embedding-compression","title":"UMEC: Unified model and embedding compression for efficient recommendation systems","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/unitedqa-a-hybrid-approach-for-open-domain","slug":"unitedqa-a-hybrid-approach-for-open-domain","title":"UnitedQA: A Hybrid Approach for Open Domain Question Answering","date":"2021-01-01","arxiv_id":"2101.00178","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-sentence-representations-learning","title":"Universal Sentence Representations Learning with Conditional Masked Language Model","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vilnmn-a-neural-module-network-approach-to","title":"VilNMN: A Neural Module Network approach to Video-Grounded Language Tasks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-coupled-graph-learning-for-cross","title":"Wasserstein Coupled Graph Learning for Cross-Modal Retrieval","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-can-phase-retrieval-tell-us-about","title":"What Can Phase Retrieval Tell Us About Private Distributed Learning?","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-constant-time-adaptive-negative-sampling","title":"A Tale of Two Efficient and Informative Negative Sampling Distributions","date":"2020-12-31","arxiv_id":"2012.15843","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-global-convergence-for-low-rank-matrix","title":"Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization","date":"2020-12-31","arxiv_id":"2012.15467","repositories_listed":0,"syntology":null},{"url":"/paper/hopretriever-retrieve-hops-over-wikipedia-to","slug":"hopretriever-retrieve-hops-over-wikipedia-to","title":"HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions","date":"2020-12-31","arxiv_id":"2012.15534","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-is-knowing-fact-based-visual-question","title":"Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings","date":"2020-12-31","arxiv_id":"2012.15484","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-verification-and-reranking-for-open","title":"Joint Verification and Reranking for Open Fact Checking Over Tables","date":"2020-12-30","arxiv_id":"2012.15115","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-hashing-for-secure-multimodal-biometrics","title":"Deep Hashing for Secure Multimodal Biometrics","date":"2020-12-29","arxiv_id":"2012.14758","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-non-linear-least-squares","title":"Graph-based non-linear least squares optimization for visual place recognition in changing environments","date":"2020-12-29","arxiv_id":"2012.14766","repositories_listed":0,"syntology":null},{"url":null,"slug":"burt-bert-inspired-universal-representation-1","title":"BURT: BERT-inspired Universal Representation from Learning Meaningful Segment","date":"2020-12-28","arxiv_id":"2012.14320","repositories_listed":0,"syntology":null},{"url":null,"slug":"pivot-through-english-reliably-answering","title":"Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval","date":"2020-12-28","arxiv_id":"2012.14094","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-curse-of-dense-low-dimensional","title":"The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes","date":"2020-12-28","arxiv_id":"2012.14210","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-sentence-representation-learning","title":"Universal Sentence Representation Learning with Conditional Masked Language Model","date":"2020-12-28","arxiv_id":"2012.14388","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-graph-based-generalized-regression","title":"Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection","date":"2020-12-27","arxiv_id":"2012.13892","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-document-expansion-for-ad-hoc","title":"Neural document expansion for ad-hoc information retrieval","date":"2020-12-27","arxiv_id":"2012.14005","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-convergence-of-model-function-based","title":"Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms","date":"2020-12-24","arxiv_id":"2012.13161","repositories_listed":0,"syntology":null},{"url":null,"slug":"thuir-coliee-2020-leveraging-semantic","title":"THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment","date":"2020-12-24","arxiv_id":"2012.13102","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-predicting-the-1","title":"Understanding and Predicting Characteristics of Test Collections in Information Retrieval","date":"2020-12-24","arxiv_id":"2012.13292","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structure-aware-method-for-direct-pose","title":"A Structure-Aware Method for Direct Pose Estimation","date":"2020-12-22","arxiv_id":"2012.12360","repositories_listed":0,"syntology":null},{"url":null,"slug":"actionbert-leveraging-user-actions-for","title":"ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces","date":"2020-12-22","arxiv_id":"2012.12350","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-really-need-scene-specific-pose","title":"Do We Really Need Scene-specific Pose Encoders?","date":"2020-12-22","arxiv_id":"2012.12014","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structures-in-earth-observation-data","title":"Learning Structures in Earth Observation Data with Gaussian Processes","date":"2020-12-22","arxiv_id":"2012.11922","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-reasoning-network-for-multi-turn","title":"A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training","date":"2020-12-21","arxiv_id":"2012.11099","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-methods-for-effective-efficient-and","title":"Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval","date":"2020-12-21","arxiv_id":"2012.11685","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attention-comparison-module-for-boosting","title":"Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems","date":"2020-12-21","arxiv_id":"2012.11357","repositories_listed":0,"syntology":null},{"url":null,"slug":"unfolded-algorithms-for-deep-phase-retrieval","title":"Unfolded Algorithms for Deep Phase Retrieval","date":"2020-12-21","arxiv_id":"2012.11102","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-estimation-of-power-flows-for-smart","title":"State Estimation of Power Flows for Smart Grids via Belief Propagation","date":"2020-12-18","arxiv_id":"2012.10473","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-white-box-analysis-of-colbert","title":"A White Box Analysis of ColBERT","date":"2020-12-17","arxiv_id":"2012.09650","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-multi-agent-e-learning-recommender","title":"Adaptive Multi-Agent E-Learning Recommender Systems","date":"2020-12-17","arxiv_id":"2012.09342","repositories_listed":0,"syntology":null},{"url":null,"slug":"clique-spatiotemporal-object-re","title":"Clique: Spatiotemporal Object Re-identification at the City Scale","date":"2020-12-17","arxiv_id":"2012.09329","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-phase-retrieval-with-green-noise","title":"Robust Phase Retrieval with Green Noise Binary Masks","date":"2020-12-17","arxiv_id":"2012.09410","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-global-shape-aware-network","title":"Semi-Global Shape-aware Network","date":"2020-12-17","arxiv_id":"2012.09372","repositories_listed":0,"syntology":null},{"url":null,"slug":"checking-fact-worthiness-using-sentence","title":"Checking Fact Worthiness using Sentence Embeddings","date":"2020-12-16","arxiv_id":"2012.09263","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-retrieval-system-for-silte","title":"Information retrieval system for silte language using BM25 weighting","date":"2020-12-16","arxiv_id":"2012.08907","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-expansion-with-artificially-generated","title":"Query expansion with artificially generated texts","date":"2020-12-16","arxiv_id":"2012.08787","repositories_listed":0,"syntology":null},{"url":null,"slug":"distant-supervised-slot-filling-for-e","title":"Distant-Supervised Slot-Filling for E-Commerce Queries","date":"2020-12-15","arxiv_id":"2012.08134","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-captioning-using-pre-trained-large","title":"Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval","date":"2020-12-14","arxiv_id":"2012.07331","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyphase-a-python-package-for-x-ray-phase","title":"PyPhase -- a Python package for X-ray phase imaging","date":"2020-12-14","arxiv_id":"2012.07942","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-cross-sectional-systematic","title":"Building Cross-Sectional Systematic Strategies By Learning to Rank","date":"2020-12-13","arxiv_id":"2012.07149","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-recommendation-with-positive","title":"GAN-based Recommendation with Positive-Unlabeled Sampling","date":"2020-12-12","arxiv_id":"2012.06901","repositories_listed":0,"syntology":null},{"url":null,"slug":"kosmos-knowledge-graph-oriented-social-media","title":"KOSMOS: Knowledge-graph Oriented Social media and Mainstream media Overview System","date":"2020-12-11","arxiv_id":"2012.06209","repositories_listed":0,"syntology":null},{"url":null,"slug":"writer-identification-and-writer-retrieval","title":"Writer Identification and Writer Retrieval Based on NetVLAD with Re-ranking","date":"2020-12-11","arxiv_id":"2012.06186","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-integrated-search-framework-for-leveraging","title":"An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem","date":"2020-12-10","arxiv_id":"2012.05397","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-mvcnn-neural-architecture-search-for","title":"Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition","date":"2020-12-10","arxiv_id":"2012.05493","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedbert-a-pre-trained-biomedical-language","title":"BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR","date":"2020-12-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-composition-net-for-visual","title":"Tensor Composition Net for Visual Relationship Prediction","date":"2020-12-10","arxiv_id":"2012.05473","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-affective-aware-pseudo-association","title":"Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method","date":"2020-12-10","arxiv_id":"2012.05982","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-detection-machine-learning-algorithms","title":"Cloud detection machine learning algorithms for PROBA-V","date":"2020-12-09","arxiv_id":"2012.10396","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-noise-aware-temperature-retrieval","title":"Spatial noise-aware temperature retrieval from infrared sounder data","date":"2020-12-09","arxiv_id":"2012.05839","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-with-convolutional-networks","title":"Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval","date":"2020-12-09","arxiv_id":"2012.10395","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-topological-method-for-comparing-document","title":"A Topological Method for Comparing Document Semantics","date":"2020-12-08","arxiv_id":"2012.04203","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-invariant-autoencoders-for-signals","title":"Rotation-Invariant Autoencoders for Signals on Spheres","date":"2020-12-08","arxiv_id":"2012.04474","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-methods-for-efficient-hybrid","title":"Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval","date":"2020-12-07","arxiv_id":"2012.04468","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-gaussian-processes-for-geophysical","title":"Deep Gaussian Processes for geophysical parameter retrieval","date":"2020-12-07","arxiv_id":"2012.12099","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-functional-imaging-through","title":"High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming","date":"2020-12-07","arxiv_id":"2012.04037","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-aware-gaussian-processes-in-remote","title":"Physics-Aware Gaussian Processes in Remote Sensing","date":"2020-12-07","arxiv_id":"2012.07986","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-transition-from-short-term-to","title":"Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network","date":"2020-12-07","arxiv_id":"2012.03510","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-kernels-for-large-scale-earth","title":"Randomized kernels for large scale Earth observation applications","date":"2020-12-07","arxiv_id":"2012.03630","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-of-aboveground-crop-nitrogen","title":"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method","date":"2020-12-07","arxiv_id":"2012.05043","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-band-selection-for-vegetation","title":"Spectral band selection for vegetation properties retrieval using Gaussian processes regression","date":"2020-12-07","arxiv_id":"2012.08640","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-methods-for-dialogue-systems","title":"Data-Efficient Methods for Dialogue Systems","date":"2020-12-05","arxiv_id":"2012.02929","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spectral-estimation-framework-for-phase","title":"A Spectral Estimation Framework for Phase Retrieval via Bregman Divergence Minimization","date":"2020-12-03","arxiv_id":"2012.01652","repositories_listed":0,"syntology":null},{"url":"/paper/cross-modal-retrieval-and-synthesis-x-mrs","slug":"cross-modal-retrieval-and-synthesis-x-mrs","title":"Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning","date":"2020-12-02","arxiv_id":"2012.01345","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-qa-on-covid-19-domain-adaptation","title":"End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training","date":"2020-12-02","arxiv_id":"2012.01414","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-regression-evaluation-of-search-engine","title":"Linear Regression Evaluation of Search Engine Automatic Search Performance Based on Hadoop and R","date":"2020-12-02","arxiv_id":"2012.02629","repositories_listed":0,"syntology":null},{"url":"/paper/100000-podcasts-a-spoken-english-document","slug":"100000-podcasts-a-spoken-english-document","title":"100,000 Podcasts: A Spoken English Document Corpus","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"3218ir-at-semeval-2020-task-11-conv1d-and","title":"3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-case-study-of-nlg-from-multimedia-data","title":"A Case Study of NLG from Multimedia Data Sources: Generating Architectural Landmark Descriptions","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-metric-learning-method-for-biomedical","title":"A Deep Metric Learning Method for Biomedical Passage Retrieval","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rule-based-lightweight-bengali-stemmer","title":"A Rule Based Lightweight Bengali Stemmer","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semi-supervised-bert-approach-for-arabic","title":"A Semi-Supervised BERT Approach for Arabic Named Entity Recognition","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"answering-legal-questions-by-learning-neural","title":"Answering Legal Questions by Learning Neural Attentive Text Representation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"askme-a-lapps-grid-based-nlp-query-and","title":"AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-evaluation-vs-user-preference-in","title":"Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"character-alignment-in-morphologically","title":"Character Alignment in Morphologically Complex Translation Sets for Related Languages","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-probabilistic-distributional-and","title":"Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conic-descent-and-its-application-to-memory","title":"Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"coreference-information-guides-human","title":"Coreference information guides human expectations during natural reading","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"covidlies-detecting-covid-19-misinformation","title":"COVIDLies: Detecting COVID-19 Misinformation on Social Media","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-hybrid-algorithm-for-automatic","title":"Development of Hybrid Algorithm for Automatic Extraction of Multiword Expressions from Monolingual and Parallel Corpus of English and Punjabi","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-top-k-with-optimal-transport","title":"Differentiable Top-k with Optimal Transport","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-transfer-based-data-augmentation-for","title":"Domain Transfer based Data Augmentation for Neural Query Translation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"frugal-neural-reranking-evaluation-on-the","title":"Frugal neural reranking: evaluation on the Covid-19 literature","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hitting-the-high-notes-subset-selection-for","title":"Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-external-event-knowledge-for","title":"Integrating External Event Knowledge for Script Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"invertible-tree-embeddings-using-a","title":"Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enhanced-named-entity","title":"Knowledge-Enhanced Named Entity Disambiguation for Short Text","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-disentangled-latent-factors-from","title":"Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach","date":"2020-12-01","arxiv_id":"2012.00682","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-health-bots-from-training-data-that","title":"Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mtm-dataset-for-joint-representation-learning","title":"MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio","date":"2020-12-01","arxiv_id":"2012.00290","repositories_listed":0,"syntology":null}],"record_sha256":"ae282478b612412c8e4de0b3f93e3c85e1c34e641d2f2e51a6945adadf908169","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}