{"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/domain-adaptation/papers/59","list_of":"/task/domain-adaptation","task":"Domain Adaptation","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":59,"pages_in_order":65,"rows_per_page":100,"rows":[5801,5900],"of":6439,"counts":{"archive_papers_tagged":6439,"with_a_code_link":2400,"where_syntology_ran_a_sample":517,"not_listed_spam_title":0,"listed":6439,"listed_where_code_ran":517,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":442,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":442,"listed_every_run_a_failure_of_syntologys_instrument":75,"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/domain-adaptation","prev":"/task/domain-adaptation/papers/58","next":"/task/domain-adaptation/papers/60","papers":[{"url":null,"slug":"seernet-at-semeval-2018-task-1-domain","title":"SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets","date":"2018-04-17","arxiv_id":"1804.06137","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-alignment-of-class-prediction","title":"Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation","date":"2018-04-12","arxiv_id":"1804.04448","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-generative-domain-adaptation-networks","title":"Causal Generative Domain Adaptation Networks","date":"2018-04-12","arxiv_id":"1804.04333","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-visual-recognition-via-domain","title":"Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning","date":"2018-04-12","arxiv_id":"1804.04687","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-statistical-machine","title":"Domain Adaptation for Statistical Machine Translation","date":"2018-04-05","arxiv_id":"1804.01760","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-teacher-student-learning-for","title":"Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation","date":"2018-04-02","arxiv_id":"1804.00644","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-learning-for-spectrum","title":"Generative Adversarial Learning for Spectrum Sensing","date":"2018-04-02","arxiv_id":"1804.00709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-discriminator-cyclegan-for","title":"A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation","date":"2018-03-27","arxiv_id":"1804.00522","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-a-multi-task","title":"Unsupervised Domain Adaptation: A Multi-task Learning-based Method","date":"2018-03-25","arxiv_id":"1803.09208","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-from","title":"Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld","date":"2018-03-24","arxiv_id":"1803.09180","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-fisheye-camera-depth-estimation","title":"Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision","date":"2018-03-16","arxiv_id":"1803.06192","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-on-graphs-by-learning-1","title":"Domain Adaptation on Graphs by Learning Aligned Graph Bases","date":"2018-03-14","arxiv_id":"1803.05288","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-recommendation-via-deep-domain","title":"Cross-domain Recommendation via Deep Domain Adaptation","date":"2018-03-08","arxiv_id":"1803.03018","repositories_listed":0,"syntology":null},{"url":"/paper/adadepth-unsupervised-content-congruent","slug":"adadepth-unsupervised-content-congruent","title":"AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation","date":"2018-03-05","arxiv_id":"1803.01599","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-training-for-neural-machine-translation","title":"Joint Training for Neural Machine Translation Models with Monolingual Data","date":"2018-03-01","arxiv_id":"1803.00353","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-optimal-policies-from-observational","title":"Learning Optimal Policies from Observational Data","date":"2018-02-23","arxiv_id":"1802.08679","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-weighted-representations-for","title":"Learning Weighted Representations for Generalization Across Designs","date":"2018-02-23","arxiv_id":"1802.08598","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-label-consistent-domain","title":"Discriminative Label Consistent Domain Adaptation","date":"2018-02-21","arxiv_id":"1802.08077","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-synthesis-for-deep-3d-human-pose","title":"Image-based Synthesis for Deep 3D Human Pose Estimation","date":"2018-02-12","arxiv_id":"1802.04216","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-visual-domain-adaptation-a-survey","title":"Deep Visual Domain Adaptation: A Survey","date":"2018-02-10","arxiv_id":"1802.03601","repositories_listed":0,"syntology":null},{"url":null,"slug":"invertible-autoencoder-for-domain-adaptation","title":"Invertible Autoencoder for domain adaptation","date":"2018-02-10","arxiv_id":"1802.06869","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-deep-domain-adaptation-for","title":"Unsupervised Deep Domain Adaptation for Pedestrian Detection","date":"2018-02-09","arxiv_id":"1802.03269","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-prediction-of-intermediate-horizon","title":"Real-time Prediction of Intermediate-Horizon Automotive Collision Risk","date":"2018-02-05","arxiv_id":"1802.01532","repositories_listed":0,"syntology":null},{"url":"/paper/museum-exhibit-identification-challenge-for","slug":"museum-exhibit-identification-challenge-for","title":"Museum Exhibit Identification Challenge for Domain Adaptation and Beyond","date":"2018-02-04","arxiv_id":"1802.01093","repositories_listed":0,"syntology":null},{"url":null,"slug":"vr-goggles-for-robots-real-to-sim-domain","title":"VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control","date":"2018-02-01","arxiv_id":"1802.00265","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-adversarial-attention-alignment-for","title":"Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization","date":"2018-01-30","arxiv_id":"1801.10068","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-bayesian-transfer-learning","title":"Optimal Bayesian Transfer Learning","date":"2018-01-02","arxiv_id":"1801.00857","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-deep-reinforcement","title":"Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-autoencoders-a-flexible-meta-learning","title":"Joint autoencoders: a flexible meta-learning framework","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-optimal-transport-and-mapping-1","title":"Large Scale Optimal Transport and Mapping Estimation","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-source-domain-adaptation-with-1","title":"Multiple Source Domain Adaptation with Adversarial Learning","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-and-geometry-aware","title":"Discriminative and Geometry Aware Unsupervised Domain Adaptation","date":"2017-12-28","arxiv_id":"1712.10042","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-meets-disentangled","title":"Domain Adaptation Meets Disentangled Representation Learning and Style Transfer","date":"2017-12-25","arxiv_id":"1712.09025","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-adversarial-domain-adaptation-for","title":"Incremental Adversarial Domain Adaptation for Continually Changing Environments","date":"2017-12-20","arxiv_id":"1712.07436","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-optimal-transport","title":"Structured Optimal Transport","date":"2017-12-17","arxiv_id":"1712.06199","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-teach-computers-to-understand-art","title":"Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art","date":"2017-12-11","arxiv_id":"1712.03727","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-using-adversarial-learning","title":"Domain Adaptation Using Adversarial Learning for Autonomous Navigation","date":"2017-12-11","arxiv_id":"1712.03742","repositories_listed":0,"syntology":null},{"url":null,"slug":"stretching-domain-adaptation-how-far-is-too","title":"Stretching Domain Adaptation: How far is too far?","date":"2017-12-06","arxiv_id":"1712.02286","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-multi-domain-image-translation","title":"Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders","date":"2017-12-06","arxiv_id":"1712.02050","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-to-image-translation-for-domain","title":"Image to Image Translation for Domain Adaptation","date":"2017-12-01","arxiv_id":"1712.00479","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-on-balanced-nonlinear","title":"Matching on Balanced Nonlinear Representations for Treatment Effects Estimation","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-regularized-domain-adaptation-for","title":"Neural Regularized Domain Adaptation for Chinese Word Segmentation","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"population-matching-discrepancy-and","title":"Population Matching Discrepancy and Applications in Deep Learning","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-rwth-aachen-machine-translation-systems","title":"The RWTH Aachen Machine Translation Systems for IWSLT 2017","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-samsung-and-university-of-edinburghs","title":"The Samsung and University of Edinburgh’s submission to IWSLT17","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/road-reality-oriented-adaptation-for-semantic","slug":"road-reality-oriented-adaptation-for-semantic","title":"ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes","date":"2017-11-30","arxiv_id":"1711.11556","repositories_listed":0,"syntology":null},{"url":null,"slug":"catgan-coupled-adversarial-transfer-for","title":"Adversarial Transfer Learning for Cross-domain Visual Recognition","date":"2017-11-24","arxiv_id":"1711.08904","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-with-1","title":"Unsupervised Domain Adaptation with Similarity Learning","date":"2017-11-24","arxiv_id":"1711.08995","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-parameter-transfer-for-deep-domain","title":"Residual Parameter Transfer for Deep Domain Adaptation","date":"2017-11-21","arxiv_id":"1711.07714","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adaptation-with-domain","title":"Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition","date":"2017-11-21","arxiv_id":"1711.08010","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-reference-loss-for-unsupervised","title":"Parameter Reference Loss for Unsupervised Domain Adaptation","date":"2017-11-20","arxiv_id":"1711.07170","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-synthetic-data-addressing","title":"Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation","date":"2017-11-19","arxiv_id":"1711.06969","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-reverse-domain-adaptation-for","title":"Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training","date":"2017-11-17","arxiv_id":"1711.06606","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-generative-adversarial-networks-and-their","title":"How Generative Adversarial Networks and Their Variants Work: An Overview","date":"2017-11-16","arxiv_id":"1711.05914","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-forgetful-learning-for-domain-expansion","title":"Less-forgetful Learning for Domain Expansion in Deep Neural Networks","date":"2017-11-16","arxiv_id":"1711.05959","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-annotation-object-detection-with-web","title":"Zero-Annotation Object Detection with Web Knowledge Transfer","date":"2017-11-16","arxiv_id":"1711.05954","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fully-convolutional-tri-branch-network-fctn","title":"A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation","date":"2017-11-10","arxiv_id":"1711.03694","repositories_listed":0,"syntology":null},{"url":"/paper/marrnet-3d-shape-reconstruction-via-25d","slug":"marrnet-3d-shape-reconstruction-via-25d","title":"MarrNet: 3D Shape Reconstruction via 2.5D Sketches","date":"2017-11-08","arxiv_id":"1711.03129","repositories_listed":0,"syntology":null},{"url":"/paper/adversarial-dropout-regularization","slug":"adversarial-dropout-regularization","title":"Adversarial Dropout Regularization","date":"2017-11-05","arxiv_id":"1711.01575","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-adversarial-domain-adaptation","title":"Few-Shot Adversarial Domain Adaptation","date":"2017-11-05","arxiv_id":"1711.02536","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-domain-adaptation-for-chinese-word","title":"Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-target-features-in-neural-machine","title":"Controlling Target Features in Neural Machine Translation via Prefix Constraints","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptable-hybrid-generation-of-rdf","title":"Domain-Adaptable Hybrid Generation of RDF Entity Descriptions","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-relation-extraction","title":"Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-from-user-level-facebook","title":"Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"japanese-all-words-wsd-system-using-the-kyoto","title":"Japanese all-words WSD system using the Kyoto Text Analysis ToolKit","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-auxiliary-tasks-for-document-level","title":"Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-lattice-search-for-domain-adaptation","title":"Neural Lattice Search for Domain Adaptation in Machine Translation","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-domain-adaptation-for-deep","title":"Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation","date":"2017-10-17","arxiv_id":"1710.06422","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-domain-adaptation","title":"Open Set Domain Adaptation","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"punda-probabilistic-unsupervised-domain","title":"PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-with-copula","title":"Unsupervised Domain Adaptation with Copula Models","date":"2017-09-29","arxiv_id":"1710.00018","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-from-synthesis-to-reality","title":"Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection","date":"2017-09-24","arxiv_id":"1709.08142","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-the-impact-of-speech-recognition","title":"Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model","date":"2017-09-22","arxiv_id":"1709.07862","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-recognition-in-the-wild-a-multi","title":"Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach","date":"2017-09-07","arxiv_id":"1709.02476","repositories_listed":0,"syntology":null},{"url":null,"slug":"translating-terminological-expressions-in","title":"Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation","date":"2017-09-07","arxiv_id":"1709.02184","repositories_listed":0,"syntology":null},{"url":null,"slug":"phylogenetic-convolutional-neural-networks-in","title":"Phylogenetic Convolutional Neural Networks in Metagenomics","date":"2017-09-06","arxiv_id":"1709.02268","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-style-in-machine-translation","title":"A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-the-ttl-romanian-pos-tagger-to-the","title":"Adapting the TTL Romanian POS Tagger to the Biomedical Domain","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-threshold-detection-for-data","title":"Automatic Threshold Detection for Data Selection in Machine Translation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-multiword-expressions-bilingual","title":"Building Multiword Expressions Bilingual Lexicons for Domain Adaptation of an Example-Based Machine Translation System","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-domain-mixing-for-neural-machine","title":"Effective Domain Mixing for Neural Machine Translation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-centered-nlp-with-user-factor","title":"Human Centered NLP with User-Factor Adaptation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nrc-machine-translation-system-for-wmt-2017","title":"NRC Machine Translation System for WMT 2017","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-model-per-entity-using-hundreds-of","title":"One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pjiitas-systems-for-wmt-2017-conference","title":"PJIIT's systems for WMT 2017 Conference","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-based-genre-identification-for-pos","title":"Similarity Based Genre Identification for POS Tagging Experts \\& Dependency Parsing","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-karlsruhe-institute-of-technology-systems-2","title":"The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-context-character-embeddings-for-chinese","title":"Word-Context Character Embeddings for Chinese Word Segmentation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-svm-learning-with-privileged","title":"Adaptive SVM+: Learning with Privileged Information for Domain Adaptation","date":"2017-08-30","arxiv_id":"1708.09083","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-domain-adaptation-via-teacher","title":"Large-Scale Domain Adaptation via Teacher-Student Learning","date":"2017-08-17","arxiv_id":"1708.05466","repositories_listed":0,"syntology":null},{"url":"/paper/webvision-database-visual-learning-and","slug":"webvision-database-visual-learning-and","title":"WebVision Database: Visual Learning and Understanding from Web Data","date":"2017-08-09","arxiv_id":"1708.02862","repositories_listed":0,"syntology":null},{"url":null,"slug":"gplac-generalizing-vision-based-robotic","title":"GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images","date":"2017-08-07","arxiv_id":"1708.02313","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-face","title":"Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos","date":"2017-08-07","arxiv_id":"1708.02191","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-umd-neural-machine-translation-systems-at","title":"The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task","date":"2017-08-03","arxiv_id":"1708.01318","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-for-cross-domain","title":"Adversarial Training for Cross-Domain Universal Dependency Parsing","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-weighting-for-neural-machine-translation","title":"Cost Weighting for Neural Machine Translation Domain Adaptation","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-language-learning-with-adversarial","title":"Cross-language Learning with Adversarial Neural Networks","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-cross-lingual-semantic-divergence","title":"Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-as-causal-inference","title":"Feature Selection as Causal Inference: Experiments with Text Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"9f4eedb56da28ba59290d002fc0cde4077877e09282e58d5cbdfa5bdf0d8429b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}