{"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/97","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":97,"pages_in_order":106,"rows_per_page":100,"rows":[9601,9700],"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/96","next":"/task/representation-learning/papers/98","papers":[{"url":null,"slug":"neighborhood-watch-representation-learning","title":"Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification","date":"2019-10-28","arxiv_id":"1911.07940","repositories_listed":0,"syntology":null},{"url":"/paper/skip-clip-self-supervised-spatiotemporal","slug":"skip-clip-self-supervised-spatiotemporal","title":"Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking","date":"2019-10-28","arxiv_id":"1910.12770","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-graph-convolutional-networks-to","title":"Fundamental Limits of Deep Graph Convolutional Networks","date":"2019-10-28","arxiv_id":"1910.12954","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-speech-recognition-and","title":"Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning","date":"2019-10-28","arxiv_id":"1910.12729","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-graph-attention-networks-with-large","title":"Improving Graph Attention Networks with Large Margin-based Constraints","date":"2019-10-25","arxiv_id":"1910.11945","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-with-2","title":"Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition","date":"2019-10-24","arxiv_id":"1910.13806","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-audio-music","title":"Graph Representation learning for Audio & Music genre Classification","date":"2019-10-23","arxiv_id":"1910.11117","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-xlnet-unsupervised-acoustic-model","title":"Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks","date":"2019-10-23","arxiv_id":"1910.10387","repositories_listed":0,"syntology":null},{"url":null,"slug":"drivers-drowsiness-detection-using-condition","title":"Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework","date":"2019-10-22","arxiv_id":"1910.09722","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-discovering","title":"Representation Learning for Discovering Phonemic Tone Contours","date":"2019-10-20","arxiv_id":"1910.08987","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mutual-information-maximization-perspective-1","title":"A Mutual Information Maximization Perspective of Language Representation Learning","date":"2019-10-18","arxiv_id":"1910.08350","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-feature-propagation-from-the","title":"Decoupling feature propagation from the design of graph auto-encoders","date":"2019-10-18","arxiv_id":"1910.08589","repositories_listed":0,"syntology":null},{"url":null,"slug":"relational-graph-representation-learning-for","title":"Relational Graph Representation Learning for Open-Domain Question Answering","date":"2019-10-18","arxiv_id":"1910.08249","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-learning-cross-modal-perception-trace","title":"Towards Learning Cross-Modal Perception-Trace Models","date":"2019-10-18","arxiv_id":"1910.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"enforcing-linearity-in-dnn-succours","title":"Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation","date":"2019-10-17","arxiv_id":"1910.08108","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-bigger-is-not-always-better-on-finite-and","title":"Why bigger is not always better: on finite and infinite neural networks","date":"2019-10-17","arxiv_id":"1910.08013","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistency-based-semi-supervised-active-1","title":"Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost","date":"2019-10-16","arxiv_id":"1910.07153","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-guided-unsupervised-rhetorical","title":"Knowledge-guided Unsupervised Rhetorical Parsing for Text Summarization","date":"2019-10-14","arxiv_id":"1910.05915","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-pooling-multi-view-attention","title":"Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare","date":"2019-10-14","arxiv_id":"1910.06456","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-neural-decoding-using-a-diversity","title":"Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method","date":"2019-10-13","arxiv_id":"1910.05695","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-human-action-videos-by-coupling-3d","title":"Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models","date":"2019-10-12","arxiv_id":"1910.06699","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighborhood-growth-determines-geometric","title":"Neighborhood Growth Determines Geometric Priors for Relational Representation Learning","date":"2019-10-12","arxiv_id":"1910.05565","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-dualization-of-operator-valued-kernel","title":"Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses","date":"2019-10-10","arxiv_id":"1910.04621","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-closer-look-at-feature-space-data","title":"A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification","date":"2019-10-09","arxiv_id":"1910.04176","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":"on-the-interpretability-and-evaluation-of","title":"On the Interpretability and Evaluation of Graph Representation Learning","date":"2019-10-07","arxiv_id":"1910.03081","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-of-ehr-data-via-graph","title":"Representation Learning of EHR Data via Graph-Based Medical Entity Embedding","date":"2019-10-07","arxiv_id":"1910.02574","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-mutual-information-in-representation","title":"High Mutual Information in Representation Learning with Symmetric Variational Inference","date":"2019-10-04","arxiv_id":"1910.04153","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robust-representations-with-graph","title":"Learning Robust Representations with Graph Denoising Policy Network","date":"2019-10-04","arxiv_id":"1910.01784","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-wasserstein-autoencoder","title":"Stacked Wasserstein Autoencoder","date":"2019-10-04","arxiv_id":"1910.02560","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-for-ehr-signals","title":"Unsupervised Representation for EHR Signals and Codes as Patient Status Vector","date":"2019-10-04","arxiv_id":"1910.01803","repositories_listed":0,"syntology":null},{"url":null,"slug":"animating-face-using-disentangled-audio","title":"Animating Face using Disentangled Audio Representations","date":"2019-10-02","arxiv_id":"1910.00726","repositories_listed":0,"syntology":null},{"url":null,"slug":"erl-net-entangled-representation-learning-for","title":"ERL-Net: Entangled Representation Learning for Single Image De-Raining","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-syntax-representation-learning-and","title":"Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representation-learning-from-1","title":"Self-Supervised Representation Learning From Multi-Domain Data","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-hamiltonian-flows","title":"Equivariant Hamiltonian Flows","date":"2019-09-30","arxiv_id":"1909.13739","repositories_listed":0,"syntology":null},{"url":null,"slug":"spread-gram-a-spreading-activation-schema-of","title":"Spread-gram: A spreading-activation schema of network structural learning","date":"2019-09-30","arxiv_id":"1909.13581","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-align-multi-camera-domain-for","title":"Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification","date":"2019-09-29","arxiv_id":"1909.13248","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-recognition-using-1","title":"Facial Expression Recognition Using Disentangled Adversarial Learning","date":"2019-09-28","arxiv_id":"1909.13135","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-knowledge-discovery-via-low-rank","title":"Learning Robust Data Representation: A Knowledge Flow Perspective","date":"2019-09-28","arxiv_id":"1909.13123","repositories_listed":0,"syntology":null},{"url":null,"slug":"bean-interpretable-representation-learning","title":"BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization","date":"2019-09-27","arxiv_id":"1909.13698","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-user-representation-learning","title":"Federated User Representation Learning","date":"2019-09-27","arxiv_id":"1909.12535","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-low-dimensional-structures-in","title":"Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach","date":"2019-09-27","arxiv_id":"1909.12898","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-actor-critic-with-hierarchical","title":"Multi-Agent Actor-Critic with Hierarchical Graph Attention Network","date":"2019-09-27","arxiv_id":"1909.12557","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-importance-of-subword-information-for","title":"On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages","date":"2019-09-26","arxiv_id":"1909.12375","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bi-diffusion-based-layer-wise-sampling","title":"A bi-diffusion based layer-wise sampling method for deep learning in large graphs","date":"2019-09-25","arxiv_id":null,"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":"an-information-theoretic-approach-to-6","title":"An Information Theoretic Approach to Distributed Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-based-on-unsupervised","title":"Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning","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":"combining-graph-and-sequence-information-to","title":"Combining graph and sequence information to learn protein representations","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"companyname-11k-an-unsupervised","title":"{COMPANYNAME}11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"consistency-based-semi-supervised-active","title":"Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-end-to-end-unsupervised-anomaly","title":"Deep End-to-end Unsupervised Anomaly Detection","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multiple-instance-learning-with-gaussian","title":"Deep Multiple Instance Learning with Gaussian Weighting","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deeppcm-predicting-protein-ligand-binding","title":"DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminability-distillation-in-group-1","title":"Discriminability Distillation in Group Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"do-autoencoder-learning-and-intervening","title":"DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-high-dimensional-data","title":"Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-graph-representation-learning-with","title":"Empowering Graph Representation Learning with Paired Training and Graph Co-Attention","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-reinforcement-learning-to-unseen","title":"Generalizing Reinforcement Learning to Unseen Actions","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-disentangle-network-for-object","title":"Hierarchical Disentangle Network for Object Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"icnn-input-conditioned-feature-representation","title":"ICNN: INPUT-CONDITIONED FEATURE REPRESENTATION LEARNING FOR TRANSFORMATION-INVARIANT NEURAL NETWORK","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-mutual-information-estimation","title":"Improved Mutual Information Estimation","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-representation-learning-from-1","title":"LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lattice-representation-learning","title":"Lattice Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-good-policies-by-learning-good","title":"Learning Good Policies By Learning Good Perceptual Models","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-metric-learning-with","title":"MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"octave-graph-convolutional-network","title":"Octave Graph Convolutional Network","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-unintended-social-bias-of-training-1","title":"On the Unintended Social Bias of Training Language Generation Models with News Articles","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-unsupervised-supervised-risk-and-one-class","title":"On unsupervised-supervised risk and one-class neural networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"patchformer-a-neural-architecture-for-self","title":"PatchFormer: A neural architecture for self-supervised representation learning on images","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":"reject-illegal-inputs-scaling-generative","title":"Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rise-and-dise-two-frameworks-for-learning","title":"RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-discriminative-representation-learning","title":"ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-instruction-following-in-a-situated","title":"Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-natural-language-representation","title":"Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-policy-adaptation","title":"Self-Supervised Policy Adaptation","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":"/paper/the-visual-task-adaptation-benchmark-1","slug":"the-visual-task-adaptation-benchmark-1","title":"The Visual Task Adaptation Benchmark","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-interpretable-molecular-graph","title":"Towards Interpretable Molecular Graph Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"trajectory-representation-learning-for-multi","title":"Trajectory representation learning for Multi-Task NMRDPs planning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-hierarchical-graph","title":"Unsupervised Hierarchical Graph Representation Learning with Variational Bayes","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-psom-deep-probabilistic-1","title":"Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wyner-vae-a-variational-autoencoder-with","title":"Wyner VAE: A Variational Autoencoder with Succinct Common Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-policy-transfer-with-disentangled","title":"Zero-Shot Policy Transfer with Disentangled Attention","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-rgb-d-salient-object-detection","title":"CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse","date":"2019-09-20","arxiv_id":"1909.09309","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-encoder-decoder-based-generative","title":"Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning","date":"2019-09-19","arxiv_id":"1909.08797","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperlearn-a-distributed-approach-for","title":"HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities","date":"2019-09-19","arxiv_id":"1909.09252","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-representation-learning-from","title":"Large-scale representation learning from visually grounded untranscribed speech","date":"2019-09-19","arxiv_id":"1909.08782","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-electronic-health","title":"Representation Learning for Electronic Health Records","date":"2019-09-19","arxiv_id":"1909.09248","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-commentary-on-recommendations-with","title":"Research Commentary on Recommendations with Side Information: A Survey and Research Directions","date":"2019-09-19","arxiv_id":"1909.12807","repositories_listed":0,"syntology":null},{"url":"/paper/towards-shape-biased-unsupervised","slug":"towards-shape-biased-unsupervised","title":"Towards Shape Biased Unsupervised Representation Learning for Domain Generalization","date":"2019-09-18","arxiv_id":"1909.08245","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighed-domain-invariant-representation","title":"Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis","date":"2019-09-18","arxiv_id":"1909.08167","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-segmentation-of-extreme","title":"Towards Unsupervised Segmentation of Extreme Weather Events","date":"2019-09-16","arxiv_id":"1909.07520","repositories_listed":0,"syntology":null},{"url":null,"slug":"visuomotor-understanding-for-representation","title":"Visuomotor Understanding for Representation Learning of Driving Scenes","date":"2019-09-16","arxiv_id":"1909.06979","repositories_listed":0,"syntology":null},{"url":null,"slug":"pedhunter-occlusion-robust-pedestrian","title":"PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes","date":"2019-09-15","arxiv_id":"1909.06826","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-representation-learning-from","title":"State Representation Learning from Demonstration","date":"2019-09-15","arxiv_id":"1910.01738","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-in-geology-and","title":"Representation Learning in Geology and GilBERT","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"3914761f6c0b08730622870f573cb5e018a354eb7fd1980fd4f47f98c6402e79","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}