{"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/116","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":116,"pages_in_order":143,"rows_per_page":100,"rows":[11501,11600],"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/115","next":"/task/retrieval/papers/117","papers":[{"url":null,"slug":"when-deep-denoising-meets-iterative-phase","title":"When deep denoising meets iterative phase retrieval","date":"2020-03-03","arxiv_id":"2003.01792","repositories_listed":0,"syntology":null},{"url":"/paper/xgpt-cross-modal-generative-pre-training-for","slug":"xgpt-cross-modal-generative-pre-training-for","title":"XGPT: Cross-modal Generative Pre-Training for Image Captioning","date":"2020-03-03","arxiv_id":"2003.01473","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-neuromorphic-events-and-color-images","title":"Matching Neuromorphic Events and Color Images via Adversarial Learning","date":"2020-03-02","arxiv_id":"2003.00636","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-retrieval-of-non-rigid-3d-human-models","title":"Shape retrieval of non-rigid 3d human models","date":"2020-03-01","arxiv_id":"2003.08763","repositories_listed":0,"syntology":null},{"url":"/paper/word-sense-disambiguation-a-comprehensive","slug":"word-sense-disambiguation-a-comprehensive","title":"Word Sense Disambiguation: A comprehensive knowledge exploitation framework","date":"2020-02-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"class-specific-blind-deconvolutional-phase","title":"Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior","date":"2020-02-28","arxiv_id":"2002.12578","repositories_listed":0,"syntology":null},{"url":null,"slug":"dc-bert-decoupling-question-and-document-for","title":"DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding","date":"2020-02-28","arxiv_id":"2002.12591","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-estimation-error-of-general-first-order","title":"The estimation error of general first order methods","date":"2020-02-28","arxiv_id":"2002.12903","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-followup-questions-for","title":"Generating Followup Questions for Interpretable Multi-hop Question Answering","date":"2020-02-27","arxiv_id":"2002.12344","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-discrimination-and-pairwise-cnn-for","title":"Multiple Discrimination and Pairwise CNN for View-based 3D Object Retrieval","date":"2020-02-27","arxiv_id":"2002.11977","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-dimensionality-reduction-and","title":"Supervised Dimensionality Reduction and Visualization using Centroid-encoder","date":"2020-02-27","arxiv_id":"2002.11934","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-phase-retrieval-at-nano-scale-via","title":"3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow","date":"2020-02-26","arxiv_id":"2002.11785","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attack-on-deep-product","title":"Adversarial Attack on Deep Product Quantization Network for Image Retrieval","date":"2020-02-26","arxiv_id":"2002.11374","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-daseinisation-using-shannon","title":"Quantifying daseinisation using Shannon entropy","date":"2020-02-26","arxiv_id":"2002.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-loop-closure-detection-via-binary","title":"Fast Loop Closure Detection via Binary Content","date":"2020-02-25","arxiv_id":"2002.10622","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-code-generation-to-improve-code","title":"Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning","date":"2020-02-24","arxiv_id":"2002.10198","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-nepali-rule-based-stemmer-and-its","title":"A Nepali Rule Based Stemmer and its performance on different NLP applications","date":"2020-02-23","arxiv_id":"2002.09901","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multimodal-image-text-embeddings-for","title":"Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval","date":"2020-02-23","arxiv_id":"2002.10016","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-instance-level-sketch-based","title":"Fine-Grained Instance-Level Sketch-Based Video Retrieval","date":"2020-02-21","arxiv_id":"2002.09461","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convolutional-baseline-for-person-re","title":"A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions","date":"2020-02-20","arxiv_id":"2003.00808","repositories_listed":0,"syntology":null},{"url":null,"slug":"expressing-objects-just-like-words-recurrent","title":"Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching","date":"2020-02-20","arxiv_id":"2002.08510","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-sound-event-retrieval-using-a","title":"Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix","date":"2020-02-20","arxiv_id":"2002.09026","repositories_listed":0,"syntology":null},{"url":null,"slug":"processing-topical-queries-on-images-of","title":"Processing topical queries on images of historical newspaper pages","date":"2020-02-20","arxiv_id":"2002.08500","repositories_listed":0,"syntology":null},{"url":null,"slug":"regret-minimization-in-stochastic-contextual","title":"Regret Minimization in Stochastic Contextual Dueling Bandits","date":"2020-02-20","arxiv_id":"2002.08583","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-temporal-feature-aggregation-for","title":"Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos","date":"2020-02-19","arxiv_id":"2002.08097","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multi-turn-response-selection","title":"Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting","date":"2020-02-18","arxiv_id":"2002.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-top-k-operator-with-optimal","title":"Differentiable Top-k Operator with Optimal Transport","date":"2020-02-16","arxiv_id":"2002.06504","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-based-question-answering-from","title":"Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey","date":"2020-02-16","arxiv_id":"2002.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"historical-document-processing-historical","title":"Historical Document Processing: Historical Document Processing: A Survey of Techniques, Tools, and Trends","date":"2020-02-15","arxiv_id":"2002.06300","repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustic-scene-classification-using-bilinear","title":"Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network","date":"2020-02-14","arxiv_id":"2002.07065","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ontology-driven-treatment-article","title":"An Ontology-driven Treatment Article Retrieval System for Precision Oncology","date":"2020-02-13","arxiv_id":"2002.05653","repositories_listed":0,"syntology":null},{"url":null,"slug":"character-segmentation-in-asian-collectors","title":"Character Segmentation in Asian Collector's Seal Imprints: An Attempt to Retrieval Based on Ancient Character Typeface","date":"2020-02-13","arxiv_id":"2003.00831","repositories_listed":0,"syntology":null},{"url":null,"slug":"keyphrase-extraction-with-span-based-feature","title":"Keyphrase Extraction with Span-based Feature Representations","date":"2020-02-13","arxiv_id":"2002.05407","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-for-query-rewriting-in-a-spoken","title":"Pre-Training for Query Rewriting in A Spoken Language Understanding System","date":"2020-02-13","arxiv_id":"2002.05607","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-dominant-sets-and-its","title":"Constrained Dominant sets and Its applications in computer vision","date":"2020-02-12","arxiv_id":"2002.06028","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-quadratic-phase-hologram","title":"Evaluation of quadratic phase hologram calculation algorithms in the Fourier regime","date":"2020-02-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-tcav-robust-and-effective","title":"Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural Networks","date":"2020-02-10","arxiv_id":"2002.03549","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-variational-inference-for","title":"Cross-modal variational inference for bijective signal-symbol translation","date":"2020-02-10","arxiv_id":"2002.03862","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-retrieval-algorithms-on-large","title":"Optimization of Retrieval Algorithms on Large Scale Knowledge Graphs","date":"2020-02-10","arxiv_id":"2002.03686","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-tasks-for-embedding-based-large","title":"Pre-training Tasks for Embedding-based Large-scale Retrieval","date":"2020-02-10","arxiv_id":"2002.03932","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-and-efficient-data-labeling-via","title":"Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling","date":"2020-02-08","arxiv_id":"2002.05520","repositories_listed":0,"syntology":null},{"url":null,"slug":"eliminating-search-intent-bias-in-learning-to","title":"Eliminating Search Intent Bias in Learning to Rank","date":"2020-02-08","arxiv_id":"2002.03203","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-robust-multilevel-semantic-cross-modal","title":"Deep Robust Multilevel Semantic Cross-Modal Hashing","date":"2020-02-07","arxiv_id":"2002.02698","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-retrieval-for-partially-coherent","title":"Phase Retrieval for Partially Coherent Observations","date":"2020-02-07","arxiv_id":"2002.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-vlad-based-deep-hashing-for-efficient","title":"Random VLAD based Deep Hashing for Efficient Image Retrieval","date":"2020-02-06","arxiv_id":"2002.02333","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-feature-invariance-with-learned","title":"Learning Test-time Augmentation for Content-based Image Retrieval","date":"2020-02-05","arxiv_id":"2002.01642","repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-with-different-indexing","title":"Experiments with Different Indexing Techniques for Text Retrieval tasks on Gujarati Language using Bag of Words Approach","date":"2020-02-05","arxiv_id":"2002.01792","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-sample-complexity-and-optimization","title":"On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems","date":"2020-02-04","arxiv_id":"2002.01066","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-incremental-cross-modal-hashing","title":"A Novel Incremental Cross-Modal Hashing Approach","date":"2020-02-03","arxiv_id":"2002.00677","repositories_listed":0,"syntology":null},{"url":"/paper/ego-ch-dataset-and-fundamental-tasks-for","slug":"ego-ch-dataset-and-fundamental-tasks-for","title":"EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision","date":"2020-02-03","arxiv_id":"2002.00899","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-generating-a-large-number-of-gumbel-max","title":"Fast Generating A Large Number of Gumbel-Max Variables","date":"2020-02-02","arxiv_id":"2002.00413","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-embedding-for-information-retrieval","title":"Concept Embedding for Information Retrieval","date":"2020-02-01","arxiv_id":"2002.01071","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-view-enhancement-hashing-for-image","title":"Deep Multi-View Enhancement Hashing for Image Retrieval","date":"2020-02-01","arxiv_id":"2002.00169","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-music-information-retrieval","title":"Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis","date":"2020-02-01","arxiv_id":"2002.00251","repositories_listed":0,"syntology":null},{"url":null,"slug":"web-table-extraction-retrieval-and","title":"Web Table Extraction, Retrieval and Augmentation: A Survey","date":"2020-02-01","arxiv_id":"2002.00207","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-structure-discovery-from-distributions","title":"Causal Structure Discovery from Distributions Arising from Mixtures of DAGs","date":"2020-01-31","arxiv_id":"2001.11940","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-convergence-of-stochastic-gradient-3","title":"On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems","date":"2020-01-31","arxiv_id":"2001.11668","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-feature-space-learning-for","title":"Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval","date":"2020-01-30","arxiv_id":"2001.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-academic-search-using-domain","title":"Aspect-based Academic Search using Domain-specific KB","date":"2020-01-29","arxiv_id":"2001.10781","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-metric-learning-network-using-proxies","title":"Deep Metric Learning Network using Proxies for Chromosome Classification in Karyotyping Test","date":"2020-01-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memo-a-deep-network-for-flexible-combination-1","title":"MEMO: A Deep Network for Flexible Combination of Episodic Memories","date":"2020-01-29","arxiv_id":"2001.10913","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-schema-labels-to-enhance-dataset","title":"Leveraging Schema Labels to Enhance Dataset Search","date":"2020-01-27","arxiv_id":"2001.10112","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-automatic-image-annotation-model","title":"An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium","date":"2020-01-26","arxiv_id":"2001.10590","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-keypoint-based-morphological-signature-for","title":"A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis","date":"2020-01-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-noise-from-competing-neighbours-word","title":"Reducing Noise from Competing Neighbours: Word Retrieval with Lateral Inhibition in Multilink","date":"2020-01-25","arxiv_id":"2002.00730","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-local-filter-pruning-for-image","title":"Progressive Local Filter Pruning for Image Retrieval Acceleration","date":"2020-01-24","arxiv_id":"2001.08878","repositories_listed":0,"syntology":null},{"url":null,"slug":"navigation-based-candidate-expansion-and","title":"Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation","date":"2020-01-23","arxiv_id":"2001.08687","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automatic-clustering-analysis-using","title":"Towards Automatic Clustering Analysis using Traces of Information Gain: The InfoGuide Method","date":"2020-01-23","arxiv_id":"2001.08677","repositories_listed":0,"syntology":null},{"url":"/paper/imagebert-cross-modal-pre-training-with-large","slug":"imagebert-cross-modal-pre-training-with-large","title":"ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data","date":"2020-01-22","arxiv_id":"2001.07966","repositories_listed":0,"syntology":null},{"url":null,"slug":"bibliometric-enhanced-information-retrieval","title":"Bibliometric-enhanced Information Retrieval 10th Anniversary Workshop Edition","date":"2020-01-20","arxiv_id":"2001.10336","repositories_listed":0,"syntology":null},{"url":null,"slug":"ur2kid-unifying-retrieval-keypoint-detection","title":"UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision","date":"2020-01-20","arxiv_id":"2001.07252","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-metric-structured-learning-for-facial","title":"Deep Metric Structured Learning For Facial Expression Recognition","date":"2020-01-18","arxiv_id":"2001.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-on-manual-thesaurus-based-query","title":"Experiments on Manual Thesaurus based Query Expansion for Ad-hoc Monolingual Gujarati Information Retrieval Tasks","date":"2020-01-18","arxiv_id":"2001.08085","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-adversarial-network-for-zero-shot","title":"Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval","date":"2020-01-18","arxiv_id":"2001.06657","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-network-projection-in-pretrained","title":"Document Network Projection in Pretrained Word Embedding Space","date":"2020-01-16","arxiv_id":"2001.05727","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-variable-private-information-retrieval","title":"Latent-variable Private Information Retrieval","date":"2020-01-16","arxiv_id":"2001.05998","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-private-information-retrieval-from","title":"Quantum Private Information Retrieval from Coded and Colluding Servers","date":"2020-01-16","arxiv_id":"2001.05883","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-mir-tutorial","title":"Deep Learning for MIR Tutorial","date":"2020-01-15","arxiv_id":"2001.05266","repositories_listed":0,"syntology":null},{"url":null,"slug":"intensity-modulated-fiber-optic-voltage","title":"Intensity-Modulated Fiber-Optic Voltage Sensors for Power Distribution Systems","date":"2020-01-15","arxiv_id":"2001.05412","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-recall-and-tell-image-captioning-with","title":"Show, Recall, and Tell: Image Captioning with Recall Mechanism","date":"2020-01-15","arxiv_id":"2001.05876","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-evaluation-of-ranking-metrics","title":"Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data","date":"2020-01-15","arxiv_id":"2001.05414","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-correlation-quantization-hashing","title":"Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval","date":"2020-01-14","arxiv_id":"2001.04625","repositories_listed":0,"syntology":null},{"url":null,"slug":"humpty-dumpty-controlling-word-meanings-via","title":"Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning","date":"2020-01-14","arxiv_id":"2001.04935","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-product-search-relevance-in-e","title":"Modeling Product Search Relevance in e-Commerce","date":"2020-01-14","arxiv_id":"2001.04980","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-replicability-of-combining-word","title":"On the Replicability of Combining Word Embeddings and Retrieval Models","date":"2020-01-13","arxiv_id":"2001.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"cure-dataset-ladder-networks-for-audio-event","title":"CURE Dataset: Ladder Networks for Audio Event Classification","date":"2020-01-12","arxiv_id":"2001.03896","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-compression-with-isotropic","title":"Embedding Compression with Isotropic Iterative Quantization","date":"2020-01-11","arxiv_id":"2001.05314","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approaches-for-amharic-parts","title":"Machine Learning Approaches for Amharic Parts-of-speech Tagging","date":"2020-01-10","arxiv_id":"2001.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-search-for-learning","title":"Conversational Search for Learning Technologies","date":"2020-01-09","arxiv_id":"2001.02912","repositories_listed":0,"syntology":null},{"url":null,"slug":"topical-result-caching-in-web-search-engines","title":"Topical Result Caching in Web Search Engines","date":"2020-01-09","arxiv_id":"2001.03010","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-data-assimilation-and-machine","title":"Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model","date":"2020-01-06","arxiv_id":"2001.01520","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-global-and-local-consistent","title":"Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks","date":"2020-01-05","arxiv_id":"2001.01284","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-diversity-in-heterogeneous","title":"Measuring Diversity in Heterogeneous Information Networks","date":"2020-01-05","arxiv_id":"2001.01296","repositories_listed":0,"syntology":null},{"url":null,"slug":"rank-aggregation-from-pairwise-comparisons-in","title":"Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-language-model-pre","title":"Retrieval Augmented Language Model Pre-Training","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-3-d-measurement-based-on-fringe-to","title":"Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning","date":"2019-12-30","arxiv_id":"1906.05652","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-retrieval-approach-based-on-local","title":"Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information","date":"2019-12-30","arxiv_id":"1912.12978","repositories_listed":0,"syntology":null},{"url":null,"slug":"report-on-the-sigir-2019-workshop-on","title":"Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)","date":"2019-12-27","arxiv_id":"1912.12282","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-framework-for-authorship","title":"A Machine Learning Framework for Authorship Identification From Texts","date":"2019-12-21","arxiv_id":"1912.10204","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-representation-model-based-on","title":"Multimodal Prediction based on Graph Representations","date":"2019-12-21","arxiv_id":"1912.10314","repositories_listed":0,"syntology":null}],"record_sha256":"aec74655063f1ddf1ec819051ef36e24339a9a9677e8a3ea61028d127f3c07a5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}