{"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/attribute/papers/45","list_of":"/task/attribute","task":"Attribute","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":45,"pages_in_order":54,"rows_per_page":100,"rows":[4401,4500],"of":5387,"counts":{"archive_papers_tagged":5387,"with_a_code_link":1923,"where_syntology_ran_a_sample":475,"not_listed_spam_title":0,"listed":5387,"listed_where_code_ran":475,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":387,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":387,"listed_every_run_a_failure_of_syntologys_instrument":88,"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/attribute","prev":"/task/attribute/papers/44","next":"/task/attribute/papers/46","papers":[{"url":null,"slug":"local-facial-attribute-transfer-through","title":"Local Facial Attribute Transfer through Inpainting","date":"2020-02-07","arxiv_id":"2002.03040","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploratory-machine-learning-with-unknown","title":"Exploratory Machine Learning with Unknown Unknowns","date":"2020-02-05","arxiv_id":"2002.01605","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdsourced-classification-with-xor-queries","title":"Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm","date":"2020-01-31","arxiv_id":"2001.11775","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-natural-scenes-from-fmri","title":"Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN","date":"2020-01-31","arxiv_id":"2001.11761","repositories_listed":0,"syntology":null},{"url":null,"slug":"iot-behavioral-monitoring-via-network-traffic","title":"IoT Behavioral Monitoring via Network Traffic Analysis","date":"2020-01-28","arxiv_id":"2001.10632","repositories_listed":0,"syntology":null},{"url":null,"slug":"aitpr-attribute-interaction-tensor-product","title":"aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption","date":"2020-01-27","arxiv_id":"2001.09545","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-of-quantification-in-frame","title":"Introduction of Quantification in Frame Semantics","date":"2020-01-25","arxiv_id":"2002.00720","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-attribute-invertion","title":"Face Attribute Invertion","date":"2020-01-14","arxiv_id":"2001.04665","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-logic-based-relational-learning-approach-to","title":"A logic-based relational learning approach to relation extraction: The OntoILPER system","date":"2020-01-13","arxiv_id":"2001.04192","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-deep-face-recognition-via","title":"Boosting Deep Face Recognition via Disentangling Appearance and Geometry","date":"2020-01-13","arxiv_id":"2001.04559","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-guided-feature-learning-network-for","title":"Attribute-guided Feature Learning Network for Vehicle Re-identification","date":"2020-01-12","arxiv_id":"2001.03872","repositories_listed":0,"syntology":null},{"url":null,"slug":"internal-representation-dynamics-and-geometry","title":"Internal representation dynamics and geometry in recurrent neural networks","date":"2020-01-09","arxiv_id":"2001.03255","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-block-based-generative-model-for-attributed","title":"A Block-based Generative Model for Attributed Networks Embedding","date":"2020-01-06","arxiv_id":"2001.01383","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnntop-a-cnn-based-trajectory-owner","title":"CNNTOP: a CNN-based Trajectory Owner Prediction Method","date":"2020-01-05","arxiv_id":"2001.01185","repositories_listed":0,"syntology":null},{"url":"/paper/privacynet-semi-adversarial-networks-for","slug":"privacynet-semi-adversarial-networks-for","title":"PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy","date":"2020-01-02","arxiv_id":"2001.00561","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-supervised-learning-recognizing-unseen","title":"BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-missing-information-in-deep","title":"Characterizing Missing Information in Deep Networks Using Backpropagated Gradients","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-infer-user-interface-attributes-1","title":"Learning to Infer User Interface Attributes from Images","date":"2019-12-31","arxiv_id":"1912.13243","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-guided-text-structuring-in-clinical","title":"Knowledge-guided Text Structuring in Clinical Trials","date":"2019-12-28","arxiv_id":"1912.12380","repositories_listed":0,"syntology":null},{"url":null,"slug":"mulgan-facial-attribute-editing-by-exemplar","title":"MulGAN: Facial Attribute Editing by Exemplar","date":"2019-12-28","arxiv_id":"1912.12396","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-cold-start-problem-in-recommendation","title":"Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks","date":"2019-12-28","arxiv_id":"1912.12398","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-attributes-of-nodes-using-network","title":"Predicting Attributes of Nodes Using Network Structure","date":"2019-12-27","arxiv_id":"1912.12264","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-face-aging","title":"Controllable Face Aging","date":"2019-12-20","arxiv_id":"1912.09694","repositories_listed":0,"syntology":null},{"url":null,"slug":"aanet-attribute-attention-network-for-person-1","title":"AANet: Attribute Attention Network for Person Re-Identifications","date":"2019-12-19","arxiv_id":"1912.09021","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-decision-trees-for-explainable-1","title":"Meta Decision Trees for Explainable Recommendation Systems","date":"2019-12-19","arxiv_id":"1912.09140","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-robot-path-planning-via-genetic","title":"Multi-Robot Path Planning Via Genetic Programming","date":"2019-12-19","arxiv_id":"1912.09503","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-human-judgments-of-causality","title":"Understanding Human Judgments of Causality","date":"2019-12-19","arxiv_id":"1912.08998","repositories_listed":0,"syntology":null},{"url":null,"slug":"invariant-attribute-profiles-a-spatial","title":"Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification","date":"2019-12-18","arxiv_id":"1912.08847","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-synthesis-from-visual-attributes-via","title":"Facial Synthesis from Visual Attributes via Sketch using Multi-Scale Generators","date":"2019-12-17","arxiv_id":"1912.10479","repositories_listed":0,"syntology":null},{"url":null,"slug":"lamp-hq-a-large-scale-multi-pose-high-quality","title":"LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition","date":"2019-12-17","arxiv_id":"1912.07809","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-extensible-and-fast-teamed-classifiers","title":"Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks","date":"2019-12-09","arxiv_id":"1912.04423","repositories_listed":0,"syntology":null},{"url":null,"slug":"metalgan-multi-domain-label-less-image","title":"MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning","date":"2019-12-05","arxiv_id":"1912.02494","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attribute-oriented-induction-based","title":"An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance","date":"2019-12-02","arxiv_id":"1912.00662","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-glass-ceiling-for-embedding","title":"Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-independent-sentiment-analysis-using","title":"Language-Independent Sentiment Analysis Using Subjectivity and Positional Information","date":"2019-11-28","arxiv_id":"1911.12544","repositories_listed":0,"syntology":null},{"url":null,"slug":"distraction-aware-feature-learning-for-human","title":"Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism","date":"2019-11-26","arxiv_id":"1911.11351","repositories_listed":0,"syntology":null},{"url":null,"slug":"attkgcn-attribute-knowledge-graph","title":"AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification","date":"2019-11-24","arxiv_id":"1911.10544","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-symbiosis-of-attribute-prediction-and","title":"On Symbiosis of Attribute Prediction and Semantic Segmentation","date":"2019-11-23","arxiv_id":"1911.11612","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-learning-of-privacy-preserving-2","title":"Adversarial Learning of Privacy-Preserving and Task-Oriented Representations","date":"2019-11-22","arxiv_id":"1911.10143","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-aware-recommendation-with-private","title":"Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning","date":"2019-11-22","arxiv_id":"1911.09872","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-noise-robust-binary-classification","title":"Attribute noise robust binary classification","date":"2019-11-18","arxiv_id":"1911.07875","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-face-analyses-through-adversarial","title":"Multiple Face Analyses through Adversarial Learning","date":"2019-11-18","arxiv_id":"1911.07846","repositories_listed":0,"syntology":null},{"url":null,"slug":"radar-emitter-classification-with-attribute","title":"Radar Emitter Classification with Attribute-specific Recurrent Neural Networks","date":"2019-11-18","arxiv_id":"1911.07683","repositories_listed":0,"syntology":null},{"url":null,"slug":"a3gan-an-attribute-aware-attentive-generative","title":"A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging","date":"2019-11-15","arxiv_id":"1911.06531","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-with-uncertain","title":"Multi-Attribute Bayesian Optimization With Interactive Preference Learning","date":"2019-11-14","arxiv_id":"1911.05934","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ordinal-factorization-model","title":"Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation","date":"2019-11-14","arxiv_id":"1911.05909","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-news-events-from-comparable-news","title":"Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach","date":"2019-11-14","arxiv_id":"1911.06407","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-granularity-metric-learning-for","title":"Semantic Granularity Metric Learning for Visual Search","date":"2019-11-14","arxiv_id":"1911.06047","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-fine-grained-style-transfer","title":"Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles","date":"2019-11-10","arxiv_id":"1911.03914","repositories_listed":0,"syntology":null},{"url":null,"slug":"table-to-text-natural-language-generation","title":"Table-to-Text Natural Language Generation with Unseen Schemas","date":"2019-11-09","arxiv_id":"1911.03601","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-meta-learning-learning-how-to-learn","title":"Fair Meta-Learning: Learning How to Learn Fairly","date":"2019-11-06","arxiv_id":"1911.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"auditing-and-achieving-intersectional","title":"Auditing and Achieving Intersectional Fairness in Classification Problems","date":"2019-11-04","arxiv_id":"1911.01468","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-aware-sequence-network-for-review","title":"Attribute-aware Sequence Network for Review Summarization","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-based-text","title":"Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-text-style-transfer-by-using-word","title":"Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nonsense-quality-control-via-two-step-reason","title":"Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reduce-textbackslash-attribute-two-step","title":"Reduce \\& Attribute: Two-Step Authorship Attribution for Large-Scale Problems","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stick-to-the-facts-learning-towards-a","title":"Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-appearance-based-person-retrieval-in","title":"Visual Appearance Based Person Retrieval in Unconstrained Environment Videos","date":"2019-10-31","arxiv_id":"1910.14565","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013292","title":"Real-time Bidding campaigns optimization using attribute selection","date":"2019-10-29","arxiv_id":"1910.13292","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-inference-for-climate-change-events","title":"Causal inference for climate change events from satellite image time series using computer vision and deep learning","date":"2019-10-25","arxiv_id":"1910.11492","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-prototype-learning-for-zero-shot","title":"Hierarchical Prototype Learning for Zero-Shot Recognition","date":"2019-10-24","arxiv_id":"1910.11671","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-generalized-artificial-neural-network","title":"A Novel Generalized Artificial Neural Network for Mining Two-Class Datasets","date":"2019-10-23","arxiv_id":"1910.10461","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-prototype-learning-for-zero","title":"Convolutional Prototype Learning for Zero-Shot Recognition","date":"2019-10-22","arxiv_id":"1910.09728","repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-parsing-for-universal","title":"Universal Decompositional Semantic Parsing","date":"2019-10-22","arxiv_id":"1910.10138","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-transport-framework-for-zero-shot","title":"Zero-Shot Recognition via Optimal Transport","date":"2019-10-20","arxiv_id":"1910.09057","repositories_listed":0,"syntology":null},{"url":null,"slug":"sanetsuperpixel-attention-network-for-skin","title":"SANet:Superpixel Attention Network for Skin Lesion Attributes Detection","date":"2019-10-20","arxiv_id":"1910.08995","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamically-aggregating-diverse-information","title":"Dynamically Aggregating Diverse Information","date":"2019-10-15","arxiv_id":"1910.07015","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-adversarial-patches-real-world-attack-on","title":"On adversarial patches: real-world attack on ArcFace-100 face recognition system","date":"2019-10-15","arxiv_id":"1910.07067","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-are-attributes-expressed-in-face-dcnns","title":"How are attributes expressed in face DCNNs?","date":"2019-10-12","arxiv_id":"1910.05657","repositories_listed":0,"syntology":null},{"url":null,"slug":"stripe-based-and-attribute-aware-network-a","title":"Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification","date":"2019-10-12","arxiv_id":"1910.05549","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-clustering-with-multiple","title":"Fairness in Clustering with Multiple Sensitive Attributes","date":"2019-10-11","arxiv_id":"1910.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"geovisual-analytics-and-interactive-machine","title":"Geovisual Analytics and Interactive Machine Learning for Situational Awareness","date":"2019-10-11","arxiv_id":"1910.05441","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-high-order-structural-and-attribute","title":"Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding","date":"2019-10-09","arxiv_id":"1910.03891","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-few-shot-attribute-translation","title":"Semi Few-Shot Attribute Translation","date":"2019-10-08","arxiv_id":"1910.03240","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobile-app-user-attribute-prediction-by","title":"Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling","date":"2019-10-06","arxiv_id":"1910.02450","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-data-preparation-on-the","title":"The Impact of Data Preparation on the Fairness of Software Systems","date":"2019-10-05","arxiv_id":"1910.02321","repositories_listed":0,"syntology":null},{"url":null,"slug":"layout-graph-reasoning-for-fashion-landmark-1","title":"Layout-Graph Reasoning for Fashion Landmark Detection","date":"2019-10-04","arxiv_id":"1910.01923","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-generation-with-variational-recurrent","title":"Graph Generation with Variational Recurrent Neural Network","date":"2019-10-02","arxiv_id":"1910.01743","repositories_listed":0,"syntology":null},{"url":null,"slug":"iiitm-face-a-database-for-facial-attribute","title":"IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments","date":"2019-10-02","arxiv_id":"1910.01219","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-fine-grained-composition-learning","title":"Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-manipulation-generative-adversarial","title":"Attribute Manipulation Generative Adversarial Networks for Fashion Images","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multiple-attribute-perceived-network-for","title":"Deep Multiple-Attribute-Perceived Network for Real-World Texture Recognition","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"m2fpa-a-multi-yaw-multi-pitch-high-quality-1","title":"M2FPA: A Multi-Yaw Multi-Pitch High-Quality Dataset and Benchmark for Facial Pose Analysis","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-inter-and-intra-class-relations-in","title":"Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"person-search-by-text-attribute-query-as-zero","title":"Person Search by Text Attribute Query As Zero-Shot Learning","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-me-if-you-can-towards-generating","title":"Sketch Me if You Can: Towards Generating Detailed Descriptions of Object Shape by Grounding in Images and Drawings","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-latent-attribute-discovery-from","title":"Towards Latent Attribute Discovery From Triplet Similarities","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hotel2vec-learning-attribute-aware-hotel","title":"Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision","date":"2019-09-30","arxiv_id":"1910.03943","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-level-fusion-from-facial-attributes","title":"Feature Level Fusion from Facial Attributes for Face Recognition","date":"2019-09-28","arxiv_id":"1909.13126","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximal-adversarial-perturbations-for","title":"Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest","date":"2019-09-27","arxiv_id":"1909.12734","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-privacy-preservation-under","title":"Adversarial Privacy Preservation under Attribute Inference Attack","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attributed-graph-learning-with-2-d-graph","title":"Attributed Graph Learning with 2-D Graph Convolution","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fr-gan-fair-and-robust-training","title":"FR-GAN: Fair and Robust Training","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-representation-learning-by","title":"Privacy-preserving Representation Learning by Disentanglement","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regularly-varying-representation-for-sentence","title":"Regularly varying representation for sentence embedding","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-based-generalized-zero-shot","title":"Relation-based Generalized Zero-shot Classification with the Domain Discriminator on the shared representation","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stablizing-adversarial-invariance-induction","title":"Stablizing Adversarial Invariance Induction by Discriminator Matching","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fairness-accuracy-landscape-of-neural","title":"The fairness-accuracy landscape of neural classifiers","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-surprising-behavior-of-graph-neural","title":"The Surprising Behavior Of Graph Neural Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"1891f6e23ca9927669562ee3267b5b416fff752a1cf7d0684205044cc83b1e99","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}