{"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/90","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":90,"pages_in_order":143,"rows_per_page":100,"rows":[8901,9000],"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/89","next":"/task/retrieval/papers/91","papers":[{"url":null,"slug":"from-baseline-to-top-performer-a","title":"From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track","date":"2023-01-25","arxiv_id":"2301.10493","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-computed-memory-or-on-the-fly-encoding-a","title":"Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute","date":"2023-01-25","arxiv_id":"2301.10448","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-retrieval-recent-advances-and","title":"Information Retrieval: Recent Advances and Beyond","date":"2023-01-20","arxiv_id":"2301.08801","repositories_listed":0,"syntology":null},{"url":null,"slug":"keyword-embeddings-for-query-suggestion","title":"Keyword Embeddings for Query Suggestion","date":"2023-01-19","arxiv_id":"2301.08006","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-recognition-in-the-age-of-clip-billion","title":"Face Recognition in the age of CLIP & Billion image datasets","date":"2023-01-18","arxiv_id":"2301.07315","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-perceiving-video-language-pre","title":"Temporal Perceiving Video-Language Pre-training","date":"2023-01-18","arxiv_id":"2301.07463","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-aligned-feature-clustering-for","title":"Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval","date":"2023-01-17","arxiv_id":"2301.06685","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-harmonic-retrieval-of-non","title":"Super-Resolution Harmonic Retrieval of Non-Circular Signals","date":"2023-01-17","arxiv_id":"2301.06948","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-recent-advances-in-automatic-term","title":"The Recent Advances in Automatic Term Extraction: A survey","date":"2023-01-17","arxiv_id":"2301.06767","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-bandwidth-close-range-information","title":"Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning","date":"2023-01-16","arxiv_id":"2301.06496","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-noise-robustness-for-spoken-content","title":"Improving Noise Robustness for Spoken Content Retrieval using Semi-supervised ASR and N-best Transcripts for BERT-based Ranking Models","date":"2023-01-15","arxiv_id":"2301.06056","repositories_listed":0,"syntology":null},{"url":null,"slug":"gh-feat-learning-versatile-generative","title":"GH-Feat: Learning Versatile Generative Hierarchical Features from GANs","date":"2023-01-12","arxiv_id":"2301.05315","repositories_listed":0,"syntology":null},{"url":null,"slug":"much-ado-about-gender-current-practices-and","title":"Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access","date":"2023-01-12","arxiv_id":"2301.04780","repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-search-to-task","title":"Taking Search to Task","date":"2023-01-12","arxiv_id":"2301.05046","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-graph2vec-an-efficient-word-embedding","title":"Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk technique","date":"2023-01-11","arxiv_id":"2301.04312","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-semantic-communication-at-the","title":"Collaborative Semantic Communication for Edge Inference","date":"2023-01-10","arxiv_id":"2301.03996","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-backfilling-with-no-regret-for-large","title":"Metric Compatible Training for Online Backfilling in Large-Scale Retrieval","date":"2023-01-10","arxiv_id":"2301.03767","repositories_listed":0,"syntology":null},{"url":null,"slug":"pix2map-cross-modal-retrieval-for-inferring","title":"Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images","date":"2023-01-10","arxiv_id":"2301.04224","repositories_listed":0,"syntology":null},{"url":null,"slug":"cursive-caption-text-detection-in-videos","title":"Cursive Caption Text Detection in Videos","date":"2023-01-09","arxiv_id":"2301.03164","repositories_listed":0,"syntology":null},{"url":null,"slug":"logically-at-factify-2023-a-multi-modal-fact","title":"Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture","date":"2023-01-09","arxiv_id":"2301.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"machining-feature-recognition-using","title":"Machining feature recognition using descriptors with range constraints for mechanical 3D models","date":"2023-01-09","arxiv_id":"2301.03167","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-multimodal-representation-for","title":"Universal Multimodal Representation for Language Understanding","date":"2023-01-09","arxiv_id":"2301.03344","repositories_listed":0,"syntology":null},{"url":null,"slug":"inpars-light-cost-effective-unsupervised","title":"InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers","date":"2023-01-08","arxiv_id":"2301.02998","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyberloc-towards-accurate-long-term-visual","title":"CyberLoc: Towards Accurate Long-term Visual Localization","date":"2023-01-06","arxiv_id":"2301.02403","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-trajectory-word-alignments-for-video","title":"Learning Trajectory-Word Alignments for Video-Language Tasks","date":"2023-01-05","arxiv_id":"2301.01953","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-segmentation-model-focusing-on-local","title":"Topic Segmentation Model Focusing on Local Context","date":"2023-01-05","arxiv_id":"2301.01935","repositories_listed":0,"syntology":null},{"url":null,"slug":"beef-up-mmwave-dense-cellular-networks-with","title":"Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching","date":"2023-01-02","arxiv_id":"2301.01141","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamic-dual-processing-object-detection","title":"A Dynamic Dual-Processing Object Detection Framework Inspired by the Brain's Recognition Mechanism","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"alignment-before-aggregation-trajectory","title":"Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clipping-distilling-clip-based-models-with-a","title":"CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-hashing-with-minimal-distance-separated","title":"Deep Hashing With Minimal-Distance-Separated Hash Centers","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-semi-supervised-metric-learning-with-1","title":"Deep Semi-Supervised Metric Learning With Mixed Label Propagation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"democratising-2d-sketch-to-3d-shape-retrieval","title":"Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-representation-learning-for-2","title":"Disentangled Representation Learning for Unsupervised Neural Quantization","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-alignment-unsupervised-domain-adaptation","title":"Dual Alignment Unsupervised Domain Adaptation for Video-Text Retrieval","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gafnet-a-global-fourier-self-attention-based","title":"GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hivlp-hierarchical-interactive-video-language","title":"HiVLP: Hierarchical Interactive Video-Language Pre-Training","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-as-a-foreign-language-beit-pretraining-1","title":"Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-skeleton-aware-3d-point-cloud","title":"Learnable Skeleton-Aware 3D Point Cloud Sampling","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-attribute-and-class-specific","title":"Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-concordant-attention-via-target","title":"Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lecture-presentations-multimodal-dataset","title":"Lecture Presentations Multimodal Dataset: Towards Understanding Multimodality in Educational Videos","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"misalign-contrast-then-distill-rethinking","title":"Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilateral-semantic-relations-modeling-for","title":"Multilateral Semantic Relations Modeling for Image Text Retrieval","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"occ-2net-robust-image-matching-based-on-3d","title":"Occ^2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-fine-grained-retrieval-via-prompting","title":"Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/pidro-parallel-isomeric-attention-with","slug":"pidro-parallel-isomeric-attention-with","title":"PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ra-clip-retrieval-augmented-contrastive","title":"RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"slan-self-locator-aided-network-for-vision","title":"SLAN: Self-Locator Aided Network for Vision-Language Understanding","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"svgformer-representation-learning-for","title":"SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"teacher-generated-spatial-attention-labels","title":"Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/vilem-visual-language-error-modeling-for","slug":"vilem-visual-language-error-modeling-for","title":"ViLEM: Visual-Language Error Modeling for Image-Text Retrieval","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vl-match-enhancing-vision-language","title":"VL-Match: Enhancing Vision-Language Pretraining with Token-Level and Instance-Level Matching","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-rotation-invariance-with-point","title":"Rethinking Rotation Invariance with Point Cloud Registration","date":"2022-12-31","arxiv_id":"2301.00149","repositories_listed":0,"syntology":null},{"url":null,"slug":"ta-da-topic-aware-domain-adaptation-for","title":"TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)","date":"2022-12-30","arxiv_id":"2301.06902","repositories_listed":0,"syntology":null},{"url":null,"slug":"bagformer-better-cross-modal-retrieval-via","title":"BagFormer: Better Cross-Modal Retrieval via bag-wise interaction","date":"2022-12-29","arxiv_id":"2212.14322","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximizing-use-case-specificity-through","title":"Maximizing Use-Case Specificity through Precision Model Tuning","date":"2022-12-29","arxiv_id":"2212.14206","repositories_listed":0,"syntology":null},{"url":null,"slug":"customizing-knowledge-graph-embedding-to","title":"Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation","date":"2022-12-28","arxiv_id":"2212.14102","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-supporting-hierarchy-and-data","title":"Efficiently Enabling Block Semantics and Data Updates in DNA Storage","date":"2022-12-27","arxiv_id":"2212.13447","repositories_listed":0,"syntology":null},{"url":null,"slug":"cache-aided-multi-user-private-information","title":"Cache-Aided Multi-User Private Information Retrieval using PDAs","date":"2022-12-26","arxiv_id":"2212.12979","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-time-series-and-spatial-data-for","title":"Modeling Time-Series and Spatial Data for Recommendations and Other Applications","date":"2022-12-25","arxiv_id":"2212.13259","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-cache-aided-multi-user-private-information","title":"On Cache-Aided Multi-User Private Information Retrieval with Small Caches","date":"2022-12-25","arxiv_id":"2212.12888","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-a-thermodynamics-of-human","title":"Development of a Thermodynamics of Human Cognition and Human Culture","date":"2022-12-24","arxiv_id":"2212.12795","repositories_listed":0,"syntology":null},{"url":null,"slug":"rank-lime-local-model-agnostic-feature","title":"Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank","date":"2022-12-24","arxiv_id":"2212.12722","repositories_listed":0,"syntology":null},{"url":null,"slug":"supergf-unifying-local-and-global-features","title":"SuperGF: Unifying Local and Global Features for Visual Localization","date":"2022-12-23","arxiv_id":"2212.13105","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-are-lemons-purple-the-concept","title":"When are Lemons Purple? The Concept Association Bias of Vision-Language Models","date":"2022-12-22","arxiv_id":"2212.12043","repositories_listed":0,"syntology":null},{"url":null,"slug":"adam-dense-retrieval-distillation-with","title":"Adam: Dense Retrieval Distillation with Adaptive Dark Examples","date":"2022-12-20","arxiv_id":"2212.10192","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-selection-bias-in-voice","title":"Addressing the Selection Bias in Voice Assistance: Training Voice Assistance Model in Python with Equal Data Selection","date":"2022-12-20","arxiv_id":"2301.00646","repositories_listed":0,"syntology":null},{"url":"/paper/exploring-the-challenges-of-open-domain-multi","slug":"exploring-the-challenges-of-open-domain-multi","title":"Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval","date":"2022-12-20","arxiv_id":"2212.10526","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-distillation-for-long-document","title":"Fine-Grained Distillation for Long Document Retrieval","date":"2022-12-20","arxiv_id":"2212.10423","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-augmented-query-expansion-for-code","title":"Generation-Augmented Query Expansion For Code Retrieval","date":"2022-12-20","arxiv_id":"2212.10692","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-canopy-height-map-in-the","title":"High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach","date":"2022-12-20","arxiv_id":"2212.10265","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyrr-hybrid-infused-reranking-for-passage","title":"HYRR: Hybrid Infused Reranking for Passage Retrieval","date":"2022-12-20","arxiv_id":"2212.10528","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-zero-shot-transfer-for","title":"Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters","date":"2022-12-20","arxiv_id":"2212.10448","repositories_listed":0,"syntology":null},{"url":null,"slug":"tess-zero-shot-classification-via-textual","title":"Empowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification","date":"2022-12-20","arxiv_id":"2212.10391","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-adapt-or-to-annotate-challenges-and","title":"To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering","date":"2022-12-20","arxiv_id":"2212.10381","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-do-decompositions-help-for-machine","title":"When Do Decompositions Help for Machine Reading?","date":"2022-12-20","arxiv_id":"2212.10019","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-entailment-fused-t5-for-open","title":"Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension","date":"2022-12-19","arxiv_id":"2212.09353","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsi-updating-transformer-memory-with-new","title":"DSI++: Updating Transformer Memory with New Documents","date":"2022-12-19","arxiv_id":"2212.09744","repositories_listed":0,"syntology":null},{"url":null,"slug":"metaclue-towards-comprehensive-visual","title":"MetaCLUE: Towards Comprehensive Visual Metaphors Research","date":"2022-12-19","arxiv_id":"2212.09898","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-image-captioning-for-edge-devices","title":"Efficient Image Captioning for Edge Devices","date":"2022-12-18","arxiv_id":"2212.08985","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-datastore-better-translation","title":"Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation","date":"2022-12-17","arxiv_id":"2212.08822","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperbolic-hierarchical-contrastive-hashing","title":"Hyperbolic Hierarchical Contrastive Hashing","date":"2022-12-17","arxiv_id":"2212.08904","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-question-answering-performance-1","title":"PolQA: Polish Question Answering Dataset","date":"2022-12-17","arxiv_id":"2212.08897","repositories_listed":0,"syntology":null},{"url":null,"slug":"rise-leveraging-retrieval-techniques-for","title":"RISE: Leveraging Retrieval Techniques for Summarization Evaluation","date":"2022-12-17","arxiv_id":"2212.08775","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-leveraging-latent-knowledge-and","title":"Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering","date":"2022-12-17","arxiv_id":"2212.08946","repositories_listed":0,"syntology":null},{"url":null,"slug":"hgan-hierarchical-graph-alignment-network-for","title":"HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval","date":"2022-12-16","arxiv_id":"2212.08281","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepjoin-joinable-table-discovery-with-pre","title":"DeepJoin: Joinable Table Discovery with Pre-trained Language Models","date":"2022-12-15","arxiv_id":"2212.07588","repositories_listed":0,"syntology":null},{"url":"/paper/fido-fusion-in-decoder-optimized-for-stronger","slug":"fido-fusion-in-decoder-optimized-for-stronger","title":"FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference","date":"2022-12-15","arxiv_id":"2212.08153","repositories_listed":0,"syntology":null},{"url":null,"slug":"political-and-economic-patterns-in-covid-19","title":"Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination","date":"2022-12-15","arxiv_id":"2212.13875","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-based-disentanglement-with-distant","title":"Retrieval-based Disentangled Representation Learning with Natural Language Supervision","date":"2022-12-15","arxiv_id":"2212.07699","repositories_listed":0,"syntology":null},{"url":null,"slug":"writer-retrieval-and-writer-identification-in","title":"Writer Retrieval and Writer Identification in Greek Papyri","date":"2022-12-15","arxiv_id":"2212.07664","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogqae-n-to-n-question-answer-pair","title":"DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog","date":"2022-12-14","arxiv_id":"2212.07112","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainability-of-text-processing-and","title":"Explainability of Text Processing and Retrieval Methods: A Critical Survey","date":"2022-12-14","arxiv_id":"2212.07126","repositories_listed":0,"syntology":null},{"url":null,"slug":"nlip-noise-robust-language-image-pre-training","title":"NLIP: Noise-robust Language-Image Pre-training","date":"2022-12-14","arxiv_id":"2212.07086","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-infinite-index-information-retrieval-on","title":"The Infinite Index: Information Retrieval on Generative Text-To-Image Models","date":"2022-12-14","arxiv_id":"2212.07476","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentive-deep-neural-networks-for-legal","title":"Attentive Deep Neural Networks for Legal Document Retrieval","date":"2022-12-13","arxiv_id":"2212.13899","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-labelling-of-bug-report-using-natural","title":"Auto-labelling of Bug Report using Natural Language Processing","date":"2022-12-13","arxiv_id":"2212.06334","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-dense-retrieval-through","title":"Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling","date":"2022-12-13","arxiv_id":"2212.06552","repositories_listed":0,"syntology":null},{"url":null,"slug":"changes-in-power-and-information-flow-in","title":"Changes in Power and Information Flow in Resting-state EEG by Working Memory Process","date":"2022-12-12","arxiv_id":"2212.05654","repositories_listed":0,"syntology":null}],"record_sha256":"7b01fcdc18abf7d02f4b9a855e92ac7cbde34368f6232e5d4e7c94e4fbab040c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}