{"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/representation-learning/papers/98","list_of":"/task/representation-learning","task":"Representation Learning","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":98,"pages_in_order":106,"rows_per_page":100,"rows":[9701,9800],"of":10580,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"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/representation-learning","prev":"/task/representation-learning/papers/97","next":"/task/representation-learning/papers/99","papers":[{"url":null,"slug":"distributed-representation-of-patients-and","title":"Distributed representation of patients and its use for medical cost prediction","date":"2019-09-13","arxiv_id":"1909.07157","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-and-visual-similarities-for","title":"Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training","date":"2019-09-13","arxiv_id":"1909.12916","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizable-forgery-detection-with","title":"Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder","date":"2019-09-13","arxiv_id":"1909.05999","repositories_listed":0,"syntology":null},{"url":null,"slug":"anonymising-queries-by-semantic-decomposition","title":"Query Obfuscation Semantic Decomposition","date":"2019-09-12","arxiv_id":"1909.05819","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-first-order-symbolic-planning","title":"Learning First-Order Symbolic Representations for Planning from the Structure of the State Space","date":"2019-09-12","arxiv_id":"1909.05546","repositories_listed":0,"syntology":null},{"url":"/paper/reasoning-over-semantic-level-graph-for-fact","slug":"reasoning-over-semantic-level-graph-for-fact","title":"Reasoning Over Semantic-Level Graph for Fact Checking","date":"2019-09-09","arxiv_id":"1909.03745","repositories_listed":0,"syntology":null},{"url":"/paper/auto-gnn-neural-architecture-search-of-graph","slug":"auto-gnn-neural-architecture-search-of-graph","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","date":"2019-09-07","arxiv_id":"1909.03184","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-clustering-of-quantitative","title":"Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model","date":"2019-09-06","arxiv_id":"1909.02953","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-video-representation-learning","title":"Discriminative Video Representation Learning Using Support Vector Classifiers","date":"2019-09-05","arxiv_id":"1909.02856","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-preliminary-study-of-disentanglement-with-1","title":"A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics","date":"2019-09-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-deep-learning-for-mental-disorders","title":"Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples","date":"2019-09-03","arxiv_id":"1909.01067","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-event-and-temporal-relation-extraction","title":"Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction","date":"2019-09-02","arxiv_id":"1909.05360","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-representation-learning-for-text","title":"Adversarial Representation Learning for Text-to-Image Matching","date":"2019-08-28","arxiv_id":"1908.10534","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representation-learning-via","title":"Self-Supervised Representation Learning via Neighborhood-Relational Encoding","date":"2019-08-27","arxiv_id":"1908.10455","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-modeling-with-syntax-aware-variational","title":"Text Modeling with Syntax-Aware Variational Autoencoders","date":"2019-08-27","arxiv_id":"1908.09964","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-domain-adaptation-for-machine","title":"Adversarial Domain Adaptation for Machine Reading Comprehension","date":"2019-08-24","arxiv_id":"1908.09209","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-with-autoencoders-for","title":"Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study","date":"2019-08-24","arxiv_id":"1908.09174","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-with-deep-structured-scale","title":"Crowd Counting with Deep Structured Scale Integration Network","date":"2019-08-23","arxiv_id":"1908.08692","repositories_listed":0,"syntology":null},{"url":null,"slug":"motif2vec-motif-aware-node-representation","title":"motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks","date":"2019-08-22","arxiv_id":"1908.08227","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807860","title":"Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery","date":"2019-08-21","arxiv_id":"1908.07860","repositories_listed":0,"syntology":null},{"url":null,"slug":"hebbian-graph-embeddings","title":"Hebbian Graph Embeddings","date":"2019-08-21","arxiv_id":"1908.08037","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-bed-pressure-based-pose-estimation-using","title":"In-bed Pressure-based Pose Estimation using Image Space Representation Learning","date":"2019-08-21","arxiv_id":"1908.08919","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807646","title":"Communal Domain Learning for Registration in Drifted Image Spaces","date":"2019-08-20","arxiv_id":"1908.07646","repositories_listed":0,"syntology":null},{"url":null,"slug":"cbowra-a-representation-learning-approach-for","title":"CBOWRA: A Representation Learning Approach for Medication Anomaly Detection","date":"2019-08-20","arxiv_id":"1908.07147","repositories_listed":0,"syntology":null},{"url":null,"slug":"expected-path-length-on-random-manifolds","title":"Expected path length on random manifolds","date":"2019-08-20","arxiv_id":"1908.07377","repositories_listed":0,"syntology":null},{"url":null,"slug":"megan-a-generative-adversarial-network-for","title":"MEGAN: A Generative Adversarial Network for Multi-View Network Embedding","date":"2019-08-20","arxiv_id":"1909.01084","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-with-high-order-feature","title":"Feature Interaction-aware Graph Neural Networks","date":"2019-08-19","arxiv_id":"1908.07110","repositories_listed":0,"syntology":null},{"url":null,"slug":"chainnet-learning-on-blockchain-graphs-with","title":"ChainNet: Learning on Blockchain Graphs with Topological Features","date":"2019-08-18","arxiv_id":"1908.06971","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-health-monitoring-of-cantilever","title":"Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning","date":"2019-08-17","arxiv_id":"1908.06326","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-co-purchases-reveal-preferences","title":"Recommendation with Attribute-aware Product Networks: A Representation Learning Model","date":"2019-08-16","arxiv_id":"1908.05928","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-the-use-of-temporal-difference","title":"Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures","date":"2019-08-15","arxiv_id":"1908.05751","repositories_listed":0,"syntology":null},{"url":null,"slug":"honem-network-embedding-using-higher-order","title":"HONEM: Learning Embedding for Higher Order Networks","date":"2019-08-15","arxiv_id":"1908.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-federated-phenotyping-and-patient-1","title":"Two-stage Federated Phenotyping and Patient Representation Learning","date":"2019-08-14","arxiv_id":"1908.05596","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-target-oriented-dual-attention-for","title":"Learning Target-oriented Dual Attention for Robust RGB-T Tracking","date":"2019-08-12","arxiv_id":"1908.04441","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-beta-or-not-to-beta-information-bottleneck","title":"To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics","date":"2019-08-11","arxiv_id":"1908.03864","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-influence-based-attentive-mavens","title":"Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation","date":"2019-08-10","arxiv_id":"1909.01079","repositories_listed":0,"syntology":null},{"url":"/paper/transferable-representation-learning-in","slug":"transferable-representation-learning-in","title":"Transferable Representation Learning in Vision-and-Language Navigation","date":"2019-08-09","arxiv_id":"1908.03409","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-hierarchical-representation-learning","title":"Sparse hierarchical representation learning on molecular graphs","date":"2019-08-06","arxiv_id":"1908.02065","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-event-propagation-via-graph-biased","title":"Modeling Event Propagation via Graph Biased Temporal Point Process","date":"2019-08-05","arxiv_id":"1908.01623","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-subspace-discovery-by-block-diagonal","title":"Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation","date":"2019-08-04","arxiv_id":"1908.01266","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-depth-and-deep","title":"Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space","date":"2019-08-04","arxiv_id":"1908.01367","repositories_listed":0,"syntology":null},{"url":null,"slug":"carl-aggregated-search-with-context-aware","title":"CARL: Aggregated Search with Context-Aware Module Embedding Learning","date":"2019-08-03","arxiv_id":"1908.03141","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-low-order-and-higher-order-graph","title":"Hybrid Low-order and Higher-order Graph Convolutional Networks","date":"2019-08-02","arxiv_id":"1908.00673","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-named-entity-recognition-what-works","title":"Arabic Named Entity Recognition: What Works and What's Next","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-small-graph-and","title":"Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview","date":"2019-08-01","arxiv_id":"1908.00187","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-of-the-4th-workshop-on-1","title":"Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-and","title":"Unsupervised Representation Learning and Anomaly Detection in ECG Sequences","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-self-supervised-learning-for-human","title":"Multi-task Self-Supervised Learning for Human Activity Detection","date":"2019-07-27","arxiv_id":"1907.11879","repositories_listed":0,"syntology":null},{"url":null,"slug":"production-ranking-systems-a-review","title":"Production Ranking Systems: A Review","date":"2019-07-24","arxiv_id":"1907.12372","repositories_listed":0,"syntology":null},{"url":null,"slug":"terminal-prediction-as-an-auxiliary-task-for","title":"Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning","date":"2019-07-24","arxiv_id":"1907.10827","repositories_listed":0,"syntology":null},{"url":null,"slug":"agent-modeling-as-auxiliary-task-for-deep","title":"Agent Modeling as Auxiliary Task for Deep Reinforcement Learning","date":"2019-07-22","arxiv_id":"1907.09597","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-convolutional-neural-network","title":"Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images","date":"2019-07-22","arxiv_id":"1907.09478","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperlink-regression-via-bregman-divergence","title":"Hyperlink Regression via Bregman Divergence","date":"2019-07-22","arxiv_id":"1908.02573","repositories_listed":0,"syntology":null},{"url":null,"slug":"product-of-orthogonal-spheres","title":"Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning","date":"2019-07-22","arxiv_id":"1907.09554","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unsupervised-character-aware-neural","title":"An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning","date":"2019-07-19","arxiv_id":"1908.01819","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-classical","title":"Representation Learning for Classical Planning from Partially Observed Traces","date":"2019-07-19","arxiv_id":"1907.08352","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnability-for-the-information-bottleneck","title":"Learnability for the Information Bottleneck","date":"2019-07-17","arxiv_id":"1907.07331","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeptrax-embedding-graphs-of-financial","title":"DeepTrax: Embedding Graphs of Financial Transactions","date":"2019-07-16","arxiv_id":"1907.07225","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-concept-representation-learning-from-1","title":"Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment","date":"2019-07-15","arxiv_id":"1907.06600","repositories_listed":0,"syntology":null},{"url":null,"slug":"discorl-continual-reinforcement-learning-via","title":"DisCoRL: Continual Reinforcement Learning via Policy Distillation","date":"2019-07-11","arxiv_id":"1907.05855","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-aware-graph-convolutional-network-not","title":"Label-Aware Graph Convolutional Networks","date":"2019-07-10","arxiv_id":"1907.04707","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-error-in-the-presence-of","title":"Quantifying Error in the Presence of Confounders for Causal Inference","date":"2019-07-10","arxiv_id":"1907.04805","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-metric-learning-for-few-shot-image","title":"Revisiting Metric Learning for Few-Shot Image Classification","date":"2019-07-06","arxiv_id":"1907.03123","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantum-field-theory-of-representation","title":"A Quantum Field Theory of Representation Learning","date":"2019-07-04","arxiv_id":"1907.02163","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-coupled-representation-learning-for","title":"Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information","date":"2019-07-04","arxiv_id":"1907.02511","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-for-learning-program","title":"Learning Blended, Precise Semantic Program Embeddings","date":"2019-07-03","arxiv_id":"1907.02136","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-news-recommendation-with-topic-aware","title":"Neural News Recommendation with Topic-Aware News Representation","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-representation-learning-of-biomedical","title":"Robust Representation Learning of Biomedical Names","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-representation-learning-for-sparse","title":"Soft Representation Learning for Sparse Transfer","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spherere-distinguishing-lexical-relations","title":"SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-lingual-representation","title":"Unsupervised Cross-Lingual Representation Learning","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-perceived-images-from-brain","title":"Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning","date":"2019-06-27","arxiv_id":"1906.12181","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-of-music-using-artist-1","title":"Representation Learning of Music Using Artist, Album, and Track Information","date":"2019-06-27","arxiv_id":"1906.11783","repositories_listed":0,"syntology":null},{"url":null,"slug":"tuning-free-disentanglement-via-projection","title":"Tuning-Free Disentanglement via Projection","date":"2019-06-27","arxiv_id":"1906.11732","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-driven-common-representation-learning","title":"Task-Driven Common Representation Learning via Bridge Neural Network","date":"2019-06-26","arxiv_id":"1906.10897","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cyclically-trained-adversarial-network-for","title":"A Cyclically-Trained Adversarial Network for Invariant Representation Learning","date":"2019-06-21","arxiv_id":"1906.09313","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-behaviour-via-intrinsic-reward-a","title":"Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study","date":"2019-06-19","arxiv_id":"1906.07865","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-task-specific-privacy","title":"Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation","date":"2019-06-19","arxiv_id":"1906.07902","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-adversarial-training-and","title":"Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling","date":"2019-06-17","arxiv_id":"1906.07234","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-unsupervised-subword-modeling-via","title":"Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation","date":"2019-06-17","arxiv_id":"1906.07245","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-with-joint-representation","title":"Anomaly Detection with Joint Representation Learning of Content and Connection","date":"2019-06-16","arxiv_id":"1906.12328","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-bidirectional-transformer-for","title":"Learning Video Representations using Contrastive Bidirectional Transformer","date":"2019-06-13","arxiv_id":"1906.05743","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-illicit-accounts-in-large-scale-e","title":"Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach","date":"2019-06-13","arxiv_id":"1906.05546","repositories_listed":0,"syntology":null},{"url":"/paper/learning-spatio-temporal-representation-with-3","slug":"learning-spatio-temporal-representation-with-3","title":"Learning Spatio-Temporal Representation with Local and Global Diffusion","date":"2019-06-13","arxiv_id":"1906.05571","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcef-deep-collaborative-encoder-framework-for","title":"Multi-local Collaborative AutoEncoder","date":"2019-06-12","arxiv_id":"1906.05173","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-words-and","title":"Representation Learning for Words and Entities","date":"2019-06-12","arxiv_id":"1906.05651","repositories_listed":0,"syntology":null},{"url":null,"slug":"warping-resilient-time-series-embeddings","title":"Warping Resilient Scalable Anomaly Detection in Time Series","date":"2019-06-12","arxiv_id":"1906.05205","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-reinforcement-learning-deployed-in","title":"Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer","date":"2019-06-11","arxiv_id":"1906.04452","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-spatio-temporal-data-mining","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","date":"2019-06-11","arxiv_id":"1906.04928","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-reinforcement-learning-informed","title":"A Survey of Reinforcement Learning Informed by Natural Language","date":"2019-06-10","arxiv_id":"1906.03926","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-goal-exploration-using-learned","title":"Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots","date":"2019-06-10","arxiv_id":"1906.03967","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-to-architect-ai-safety-defense-to","title":"Strategies to architect AI Safety: Defense to guard AI from Adversaries","date":"2019-06-08","arxiv_id":"1906.03466","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-losses-for-unlabeled-video","title":"Evolving Losses for Unlabeled Video Representation Learning","date":"2019-06-07","arxiv_id":"1906.03248","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-visual-knowledge-from-the-internet","title":"Extracting Visual Knowledge from the Internet: Making Sense of Image Data","date":"2019-06-07","arxiv_id":"1906.03219","repositories_listed":0,"syntology":null},{"url":null,"slug":"shared-private-bilingual-word-embeddings-for","title":"Shared-Private Bilingual Word Embeddings for Neural Machine Translation","date":"2019-06-07","arxiv_id":"1906.03100","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-of-dna","title":"Unsupervised Representation Learning of DNA Sequences","date":"2019-06-07","arxiv_id":"1906.03087","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepmdp-learning-continuous-latent-space","title":"DeepMDP: Learning Continuous Latent Space Models for Representation Learning","date":"2019-06-06","arxiv_id":"1906.02736","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexibly-fair-representation-learning-by","title":"Flexibly Fair Representation Learning by Disentanglement","date":"2019-06-06","arxiv_id":"1906.02589","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-deep-dual-networks-for-text","title":"Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction","date":"2019-06-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quaternion-collaborative-filtering-for","title":"Quaternion Collaborative Filtering for Recommendation","date":"2019-06-06","arxiv_id":"1906.02594","repositories_listed":0,"syntology":null}],"record_sha256":"bcf670e47d33335da26f481148637b54260973256390e872c05af568afd1d1d5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}