{"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/100","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":100,"pages_in_order":143,"rows_per_page":100,"rows":[9901,10000],"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/99","next":"/task/retrieval/papers/101","papers":[{"url":null,"slug":"utilizing-wordnets-for-cognate-detection-1","title":"Utilizing Wordnets for Cognate Detection among Indian Languages","date":"2021-12-30","arxiv_id":"2112.15124","repositories_listed":0,"syntology":null},{"url":null,"slug":"literature-review-of-the-pioneering","title":"Literature Review of the Pioneering Approaches in Cloud-based Search Engines Powered by LETOR Techniques","date":"2021-12-29","arxiv_id":"2112.14444","repositories_listed":0,"syntology":null},{"url":null,"slug":"mirror-matching-document-matching-approach-in","title":"Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews","date":"2021-12-28","arxiv_id":"2112.14318","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-head-deep-metric-learning-using-global","title":"Multi-Head Deep Metric Learning Using Global and Local Representations","date":"2021-12-28","arxiv_id":"2112.14327","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-university-of-texas-at-dallas-hltri-s","title":"The University of Texas at Dallas HLTRI's Participation in EPIC-QA: Searching for Entailed Questions Revealing Novel Answer Nuggets","date":"2021-12-28","arxiv_id":"2112.13946","repositories_listed":0,"syntology":null},{"url":"/paper/a-passage-to-india-pre-trained-word-1","slug":"a-passage-to-india-pre-trained-word-1","title":"\"A Passage to India\": Pre-trained Word Embeddings for Indian Languages","date":"2021-12-27","arxiv_id":"2112.13800","repositories_listed":0,"syntology":null},{"url":null,"slug":"hard-example-guided-hashing-for-image","title":"Hard Example Guided Hashing for Image Retrieval","date":"2021-12-27","arxiv_id":"2112.13565","repositories_listed":0,"syntology":null},{"url":null,"slug":"mind-the-gap-cross-lingual-information","title":"Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement","date":"2021-12-27","arxiv_id":"2112.13510","repositories_listed":0,"syntology":null},{"url":"/paper/percqa-persian-community-question-answering","slug":"percqa-persian-community-question-answering","title":"PerCQA: Persian Community Question Answering Dataset","date":"2021-12-25","arxiv_id":"2112.13238","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-crafting-for-the-wide-multiple","title":"Learning and Crafting for the Wide Multiple Baseline Stereo","date":"2021-12-22","arxiv_id":"2112.12027","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-topic-modeling-in-twitter-through","title":"Improved Topic modeling in Twitter through Community Pooling","date":"2021-12-20","arxiv_id":"2201.00690","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-label-noise-for-image-retrieval","title":"Learning with Label Noise for Image Retrieval by Selecting Interactions","date":"2021-12-20","arxiv_id":"2112.10453","repositories_listed":0,"syntology":null},{"url":null,"slug":"mocanet-motion-retargeting-in-the-wild-via","title":"MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks","date":"2021-12-19","arxiv_id":"2112.10082","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-multi-user-semantic-1","title":"Task-Oriented Multi-User Semantic Communications","date":"2021-12-19","arxiv_id":"2112.10255","repositories_listed":0,"syntology":null},{"url":null,"slug":"best-of-both-worlds-a-hybrid-approach-for","title":"Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts","date":"2021-12-17","arxiv_id":"2201.02740","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-contrastive-learning-for-speech","title":"Cross-modal Contrastive Learning for Speech Translation","date":"2021-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nearest-neighbor-search-with-compact-codes-a","title":"Nearest neighbor search with compact codes: A decoder perspective","date":"2021-12-17","arxiv_id":"2112.09568","repositories_listed":0,"syntology":null},{"url":null,"slug":"negative-sample-is-negative-in-its-own-way-1","title":"Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval","date":"2021-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"product-information-browsing-support-system","title":"Product Information Browsing Support System Using Analytic Hierarchy Process","date":"2021-12-17","arxiv_id":"2112.09435","repositories_listed":0,"syntology":null},{"url":null,"slug":"rank4class-a-ranking-formulation-for","title":"Rank4Class: A Ranking Formulation for Multiclass Classification","date":"2021-12-17","arxiv_id":"2112.09727","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsifying-sparse-representations-for","title":"Sparsifying Sparse Representations for Passage Retrieval by Top-$k$ Masking","date":"2021-12-17","arxiv_id":"2112.09628","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-faithful-personalized-response","title":"Towards Faithful Personalized Response Selection in Retrieval Based Dialog Systems","date":"2021-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-attention-for-vision-and","title":"Understanding Attention for Vision-and-Language Tasks","date":"2021-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conqrr-conversational-query-rewriting-for","title":"CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning","date":"2021-12-16","arxiv_id":"2112.08558","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-enhancement-of-latent","title":"Self-supervised Enhancement of Latent Discovery in GANs","date":"2021-12-16","arxiv_id":"2112.08835","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-the-suitable-resampling-strategy","title":"Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties","date":"2021-12-15","arxiv_id":"2201.07932","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-mining-through-label-induction-grouping","title":"Text Mining Through Label Induction Grouping Algorithm Based Method","date":"2021-12-15","arxiv_id":"2112.08486","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-transformer-based-video-hashing","title":"Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos","date":"2021-12-15","arxiv_id":"2112.08117","repositories_listed":0,"syntology":null},{"url":null,"slug":"ace-bert-adversarial-cross-modal-enhanced","title":"ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval","date":"2021-12-14","arxiv_id":"2112.07209","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosted-dense-retriever","title":"Boosted Dense Retriever","date":"2021-12-14","arxiv_id":"2112.07771","repositories_listed":0,"syntology":null},{"url":null,"slug":"coco-bert-improving-video-language-pre","title":"CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising","date":"2021-12-14","arxiv_id":"2112.07515","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-search-with-mixed-initiative","title":"Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval","date":"2021-12-14","arxiv_id":"2112.07308","repositories_listed":0,"syntology":null},{"url":"/paper/omad-object-model-with-articulated","slug":"omad-object-model-with-articulated","title":"OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval","date":"2021-12-14","arxiv_id":"2112.07334","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-up-query-focused-summarization-to","title":"Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study","date":"2021-12-14","arxiv_id":"2112.07536","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-vs-target-word-quantifying-biases-in","title":"Measuring Context-Word Biases in Lexical Semantic Datasets","date":"2021-12-13","arxiv_id":"2112.06733","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-eeg-characteristics-during","title":"Differential EEG Characteristics during Working Memory Encoding and Re-encoding","date":"2021-12-13","arxiv_id":"2112.06464","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-unsupervised-domain-adaptive-person","title":"Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation","date":"2021-12-13","arxiv_id":"2112.06632","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-mutual-information-maximization-a","title":"Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing","date":"2021-12-13","arxiv_id":"2112.06489","repositories_listed":0,"syntology":null},{"url":null,"slug":"medgraph-an-experimental-semantic-information","title":"MedGraph: An experimental semantic information retrieval method using knowledge graph embedding for the biomedical citations indexed in PubMed","date":"2021-12-12","arxiv_id":"2112.06348","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-spatiotemporal-representation","title":"Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity","date":"2021-12-11","arxiv_id":"2112.05883","repositories_listed":0,"syntology":null},{"url":null,"slug":"match-your-words-a-study-of-lexical-matching","title":"Match Your Words! A Study of Lexical Matching in Neural Information Retrieval","date":"2021-12-10","arxiv_id":"2112.05662","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-information-retrieval-for-false-claims","title":"Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification","date":"2021-12-10","arxiv_id":"2112.07618","repositories_listed":0,"syntology":null},{"url":null,"slug":"roominoes-generating-novel-3d-floor-plans","title":"Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms","date":"2021-12-10","arxiv_id":"2112.05644","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-multimodal-pre-training-and-prompt","title":"Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation","date":"2021-12-10","arxiv_id":"2112.05587","repositories_listed":0,"syntology":null},{"url":null,"slug":"dvhn-a-deep-hashing-framework-for-large-scale","title":"DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification","date":"2021-12-09","arxiv_id":"2112.04937","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-scattered-sources-to-comprehensive","title":"From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach","date":"2021-12-09","arxiv_id":"2112.04810","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-survey-directions","title":"Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices","date":"2021-12-07","arxiv_id":"2112.03572","repositories_listed":0,"syntology":null},{"url":null,"slug":"stc-mix-space-time-channel-mixing-for-self","title":"Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning","date":"2021-12-07","arxiv_id":"2112.03906","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-equivariant-contrastive-video-1","title":"Time-Equivariant Contrastive Video Representation Learning","date":"2021-12-07","arxiv_id":"2112.03624","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sensitivity-analysis-of-the-msmarco-passage","title":"A Sensitivity Analysis of the MSMARCO Passage Collection","date":"2021-12-06","arxiv_id":"2112.03396","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-arithmetic-for-text-driven-image","title":"Embedding Arithmetic of Multimodal Queries for Image Retrieval","date":"2021-12-06","arxiv_id":"2112.03162","repositories_listed":0,"syntology":null},{"url":null,"slug":"sub-region-localized-hashing-for-fine-grained","title":"sub-region localized hashing for fine-grained image retrieval","date":"2021-12-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-query-expansion-over-the-nearest","title":"Learning Query Expansion over the Nearest Neighbor Graph","date":"2021-12-05","arxiv_id":"2112.02666","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-autoencoder-with-cca-for-audio","title":"Variational Autoencoder with CCA for Audio-Visual Cross-Modal Retrieval","date":"2021-12-05","arxiv_id":"2112.02601","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-electromagnetic-validations-of-large","title":"Fast Electromagnetic Validations of Large-Scale Digital Coding Metasurfaces Accelerated by Recurrence Rebuild and Retrieval Method","date":"2021-12-04","arxiv_id":"2112.05082","repositories_listed":0,"syntology":null},{"url":"/paper/multilingual-training-for-software","slug":"multilingual-training-for-software","title":"Multilingual training for Software Engineering","date":"2021-12-03","arxiv_id":"2112.02043","repositories_listed":0,"syntology":null},{"url":null,"slug":"music-to-dance-generation-with-optimal","title":"Music-to-Dance Generation with Optimal Transport","date":"2021-12-03","arxiv_id":"2112.01806","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-law-article-mining-based-on-deep","title":"Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code","date":"2021-12-02","arxiv_id":"2112.03033","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-method-to-disambiguate-a-word-by-using","title":"A Method to Disambiguate a Word by Using Restricted Boltzmann Machine","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-melodia-and-time","title":"Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogacts-based-search-and-retrieval-for","title":"DialogActs based Search and Retrieval for Response Generation in Conversation Systems","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-multi-linear-attention-network","title":"Generalizable Multi-linear Attention Network","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-modeling-of-visual-objects-and","title":"Joint Modeling of Visual Objects and Relations for Scene Graph Generation","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-turn-target-guided-topic-prediction","title":"Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-learning-based-deep-learning-model","title":"Multitask Learning based Deep Learning Model for Music Artist and Language Recognition","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-adaptation-generates-levy-flights-in","title":"Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-algorithms-for-stochastic-contextual","title":"Optimal Algorithms for Stochastic Contextual Preference Bandits","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ranking-distance-calibration-for-cross-domain","title":"Ranking Distance Calibration for Cross-Domain Few-Shot Learning","date":"2021-12-01","arxiv_id":"2112.00260","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-ranking-for-image-retrieval-and","title":"Re-ranking for image retrieval and transductive few-shot classification","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-topic-models-for-comparable-corpora","title":"Bilingual Topic Models for Comparable Corpora","date":"2021-11-30","arxiv_id":"2111.15278","repositories_listed":0,"syntology":null},{"url":null,"slug":"easy-semantification-of-bioassays","title":"Easy Semantification of Bioassays","date":"2021-11-30","arxiv_id":"2111.15182","repositories_listed":0,"syntology":null},{"url":null,"slug":"forgetting-leads-to-chaos-in-attractor","title":"Forgetting leads to chaos in attractor networks","date":"2021-11-30","arxiv_id":"2112.00119","repositories_listed":0,"syntology":null},{"url":null,"slug":"spaceedit-learning-a-unified-editing-space","title":"SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing","date":"2021-11-30","arxiv_id":"2112.00180","repositories_listed":0,"syntology":null},{"url":null,"slug":"harmonic-retrieval-with-l-1-tucker-tensor","title":"Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition","date":"2021-11-29","arxiv_id":"2111.14780","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-context-aware-embedding-for-person","title":"Learning Context-Aware Embedding for Person Search","date":"2021-11-29","arxiv_id":"2111.14316","repositories_listed":0,"syntology":null},{"url":null,"slug":"pgganet-pose-guided-graph-attention-network","title":"PGGANet: Pose Guided Graph Attention Network for Person Re-identification","date":"2021-11-29","arxiv_id":"2111.14411","repositories_listed":0,"syntology":null},{"url":null,"slug":"fashionsearchnet-v2-learning-attribute","title":"FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation","date":"2021-11-28","arxiv_id":"2111.14145","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-dense-retrieval-as-mixture-of","title":"Interpreting Dense Retrieval as Mixture of Topics","date":"2021-11-27","arxiv_id":"2111.13957","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-methods-in-information-retrieval","title":"Pre-training Methods in Information Retrieval","date":"2021-11-27","arxiv_id":"2111.13853","repositories_listed":0,"syntology":null},{"url":null,"slug":"blaschke-product-neural-networks-bpnn-a-1","title":"Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions","date":"2021-11-26","arxiv_id":"2111.13311","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-document-coverage-for-relation","title":"Predicting Document Coverage for Relation Extraction","date":"2021-11-26","arxiv_id":"2111.13611","repositories_listed":0,"syntology":null},{"url":null,"slug":"country-wide-retrieval-of-forest-structure","title":"Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles","date":"2021-11-25","arxiv_id":"2111.13154","repositories_listed":0,"syntology":null},{"url":null,"slug":"acnet-approaching-and-centralizing-network","title":"ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval","date":"2021-11-24","arxiv_id":"2111.12757","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-captioner-long-tail-vision-and","title":"Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets","date":"2021-11-24","arxiv_id":"2111.12727","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-main-content-extraction-on-near","title":"The Impact of Main Content Extraction on Near-Duplicate Detection","date":"2021-11-21","arxiv_id":"2111.10864","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-model-of-the-entorhinal","title":"A Neural Network Model of the Entorhinal Cortex and Hippocampus for Event-order Memory Processing","date":"2021-11-20","arxiv_id":"2111.10535","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-context-complexity-and-clustering","title":"Effects of context, complexity, and clustering on evaluation for math formula retrieval","date":"2021-11-20","arxiv_id":"2111.10504","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-augmented-learning-to-rank-for-querying","title":"Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph","date":"2021-11-20","arxiv_id":"2111.10541","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-question-answering-system-for-the","title":"Building a Question Answering System for the Manufacturing Domain","date":"2021-11-19","arxiv_id":"2111.10044","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-based-creativity-support-tools-using","title":"Sketch-based Creativity Support Tools using Deep Learning","date":"2021-11-19","arxiv_id":"2111.09991","repositories_listed":0,"syntology":null},{"url":null,"slug":"ufo-a-unified-transformer-for-vision-language","title":"UFO: A UniFied TransfOrmer for Vision-Language Representation Learning","date":"2021-11-19","arxiv_id":"2111.10023","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-build-robust-faq-chatbot-with","title":"How to Build Robust FAQ Chatbot with Controllable Question Generator?","date":"2021-11-18","arxiv_id":"2112.03007","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-building-height-retrieval-from","title":"Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks","date":"2021-11-18","arxiv_id":"2111.09460","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-and-cost-trade-offs-in-passage-re","title":"Quality and Cost Trade-offs in Passage Re-ranking Task","date":"2021-11-18","arxiv_id":"2111.09927","repositories_listed":0,"syntology":null},{"url":null,"slug":"induce-edit-retrieve-language-grounded","title":"Induce, Edit, Retrieve:Language Grounded Multimodal Schema for Instructional Video Retrieval","date":"2021-11-17","arxiv_id":"2111.09276","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-me-anything-in-your-native-language","title":"Ask Me Anything in Your Native Language","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-document-representations-for-dense","title":"Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-got-a-date-introducing-transformers-to-1","title":"BERT got a Date: Introducing Transformers to Temporal Tagging","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-of-machine-reading-systems-at","title":"Calibration of Machine Reading Systems at Scale","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-static-and-contextualised","title":"Combining static and contextualised multilingual embeddings","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"2ae3d638cdf7e8789198c551fd233ea418243ae4a3349f022f3f0cc2f20bb417","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}