{"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/prediction/papers/76","list_of":"/task/prediction","task":"Prediction","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":76,"pages_in_order":88,"rows_per_page":100,"rows":[7501,7600],"of":8760,"counts":{"archive_papers_tagged":8760,"with_a_code_link":2835,"where_syntology_ran_a_sample":607,"not_listed_spam_title":0,"listed":8760,"listed_where_code_ran":607,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":519,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":519,"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/prediction","prev":"/task/prediction/papers/75","next":"/task/prediction/papers/77","papers":[{"url":null,"slug":"neural-network-prediction-of-censorable","title":"Neural Network Prediction of Censorable Language","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-teacher-student-learning-for","title":"Progressive Teacher-Student Learning for Early Action Prediction","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-structured-prediction-using-argument","title":"SPARSE: Structured Prediction using Argument-Relative Structured Encoding","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-hybrid-model-based-on-multi-objective","title":"A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting","date":"2019-05-30","arxiv_id":"1905.13550","repositories_listed":0,"syntology":null},{"url":null,"slug":"address-instance-level-label-prediction-in","title":"Address Instance-level Label Prediction in Multiple Instance Learning","date":"2019-05-29","arxiv_id":"1905.12226","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimension-reduction-approach-for","title":"Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks","date":"2019-05-29","arxiv_id":"1905.12176","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategic-prediction-with-latent-aggregative","title":"Strategic Prediction with Latent Aggregative Games","date":"2019-05-29","arxiv_id":"1905.12169","repositories_listed":0,"syntology":null},{"url":null,"slug":"amoebacontact-and-gdfold-a-new-pipeline-for","title":"AmoebaContact and GDFold: a new pipeline for rapid prediction of protein structures","date":"2019-05-28","arxiv_id":"1905.11640","repositories_listed":0,"syntology":null},{"url":null,"slug":"educe-explaining-model-decisions-through","title":"EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction","date":"2019-05-28","arxiv_id":"1905.11852","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-calibrating-uncertainty","title":"Evaluating and Calibrating Uncertainty Prediction in Regression Tasks","date":"2019-05-28","arxiv_id":"1905.11659","repositories_listed":0,"syntology":null},{"url":null,"slug":"measurement-of-permeability-for-ferrous","title":"Measurement of permeability for ferrous metallic plates using a novel lift-off compensation technique on phase signature","date":"2019-05-28","arxiv_id":"1905.13079","repositories_listed":0,"syntology":null},{"url":null,"slug":"rare-failure-prediction-via-event-matching","title":"Rare Failure Prediction via Event Matching for Aerospace Applications","date":"2019-05-28","arxiv_id":"1905.11586","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-level-explanations-in-music-emotion","title":"Two-level Explanations in Music Emotion Recognition","date":"2019-05-28","arxiv_id":"1905.11760","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-ontologies-to-improve-performance-in-1","title":"Using Ontologies To Improve Performance In Massively Multi-label Prediction Models","date":"2019-05-28","arxiv_id":"1905.12126","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-supply-demand-prediction-for","title":"Attention-based Supply-Demand Prediction for Autonomous Vehicles","date":"2019-05-27","arxiv_id":"1905.10983","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-prediction-framework-for-semi","title":"Label Prediction Framework for Semi-Supervised Cross-Modal Retrieval","date":"2019-05-27","arxiv_id":"1905.11139","repositories_listed":0,"syntology":null},{"url":null,"slug":"star-gcn-stacked-and-reconstructed-graph","title":"STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems","date":"2019-05-27","arxiv_id":"1905.13129","repositories_listed":0,"syntology":null},{"url":null,"slug":"earthquake-prediction-with-artificial-neural","title":"Earthquake Prediction With Artificial Neural Network Method: The Application Of West Anatolian Fault In Turkey","date":"2019-05-26","arxiv_id":"1907.02209","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-autoencoders-for-lyapunov","title":"Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction","date":"2019-05-26","arxiv_id":"1905.10866","repositories_listed":0,"syntology":null},{"url":null,"slug":"usage-of-multiple-rtl-features-for-earthquake","title":"Usage of multiple RTL features for Earthquake prediction","date":"2019-05-26","arxiv_id":"1905.10805","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-distribution-free-prediction","title":"Adaptive, Distribution-Free Prediction Intervals for Deep Networks","date":"2019-05-25","arxiv_id":"1905.10634","repositories_listed":0,"syntology":null},{"url":null,"slug":"ldsm-logarithm-depth-streaming-multi-label","title":"LdSM: Logarithm-depth Streaming Multi-label Decision Trees","date":"2019-05-24","arxiv_id":"1905.10428","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-advantages-of-multiple-classes-for","title":"The advantages of multiple classes for reducing overfitting from test set reuse","date":"2019-05-24","arxiv_id":"1905.10360","repositories_listed":0,"syntology":null},{"url":null,"slug":"190513312","title":"Convolutional Restricted Boltzmann Machine Based-Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer","date":"2019-05-23","arxiv_id":"1905.13312","repositories_listed":0,"syntology":null},{"url":null,"slug":"glioma-grade-predictions-using-scattering","title":"Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics","date":"2019-05-23","arxiv_id":"1905.09589","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-retrosynthetic-pathway-planning","title":"Automatic Retrosynthetic Pathway Planning Using Template-free Models","date":"2019-05-21","arxiv_id":"1906.02308","repositories_listed":0,"syntology":null},{"url":null,"slug":"activity-recognition-and-prediction-in-real","title":"Activity Recognition and Prediction in Real Homes","date":"2019-05-20","arxiv_id":"1905.08654","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-interval-estimations","title":"Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets","date":"2019-05-20","arxiv_id":"1905.07886","repositories_listed":0,"syntology":null},{"url":null,"slug":"dance-hit-song-prediction","title":"Dance Hit Song Prediction","date":"2019-05-17","arxiv_id":"1905.08076","repositories_listed":0,"syntology":null},{"url":null,"slug":"pair-matching-when-bandits-meet-stochastic","title":"Pair-Matching: Links Prediction with Adaptive Queries","date":"2019-05-17","arxiv_id":"1905.07342","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-spatial-temporal-network-for","title":"Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction","date":"2019-05-15","arxiv_id":"1905.06335","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-motion-trajectory-prediction-a-survey","title":"Human Motion Trajectory Prediction: A Survey","date":"2019-05-15","arxiv_id":"1905.06113","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-neural-networks-for","title":"Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction","date":"2019-05-14","arxiv_id":"1905.05849","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-driven-stock-trend-prediction-and","title":"Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network","date":"2019-05-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-grouping-guided-neural-network","title":"Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction","date":"2019-05-13","arxiv_id":"1905.04872","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-spectrum-occupancy-learning-via","title":"Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks","date":"2019-05-10","arxiv_id":"1905.04392","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-judgment-prediction-via-multi","title":"Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network","date":"2019-05-10","arxiv_id":"1905.03969","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-and-outlier-detection-a","title":"Prediction and outlier detection in classification problems","date":"2019-05-10","arxiv_id":"1905.04396","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600951","title":"User Traffic Prediction for Proactive Resource Management: Learning-Powered Approaches","date":"2019-05-09","arxiv_id":"1906.00951","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-user-context-for-valence-prediction","title":"Modeling user context for valence prediction from narratives","date":"2019-05-09","arxiv_id":"1905.05701","repositories_listed":0,"syntology":null},{"url":null,"slug":"photometric-transformer-networks-and-label","title":"Photometric Transformer Networks and Label Adjustment for Breast Density Prediction","date":"2019-05-08","arxiv_id":"1905.02906","repositories_listed":0,"syntology":null},{"url":"/paper/conditional-generative-neural-system-for","slug":"conditional-generative-neural-system-for","title":"Conditional Generative Neural System for Probabilistic Trajectory Prediction","date":"2019-05-05","arxiv_id":"1905.01631","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-item-consumption-prediction-using-deep","title":"New Item Consumption Prediction Using Deep Learning","date":"2019-05-05","arxiv_id":"1905.01686","repositories_listed":0,"syntology":null},{"url":null,"slug":"meshdepth-disconnected-mesh-based-deep-depth","title":"TriDepth: Triangular Patch-based Deep Depth Prediction","date":"2019-05-03","arxiv_id":"1905.01312","repositories_listed":0,"syntology":null},{"url":null,"slug":"coordination-and-trajectory-prediction-for","title":"Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling","date":"2019-05-02","arxiv_id":"1905.00587","repositories_listed":0,"syntology":null},{"url":null,"slug":"parity-models-a-general-framework-for-coding","title":"Parity Models: A General Framework for Coding-Based Resilience in ML Inference","date":"2019-05-02","arxiv_id":"1905.00863","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-transformers-for-simple-question-1","title":"Pretrained Transformers for Simple Question Answering","date":"2019-05-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exhaustive-analysis-of-lazy-vs-eager","title":"An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-composition-of-sentence-embeddings","title":"Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-in-hypergraphs-using-graph","title":"Link Prediction in Hypergraphs using Graph Convolutional Networks","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pearl-prototype-learning-via-rule-lists","title":"Pearl: Prototype lEArning via Rule Lists","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sex-prediction-from-periocular-images-across","title":"Sex-Prediction from Periocular Images across Multiple Sensors and Spectra","date":"2019-05-01","arxiv_id":"1905.00396","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-ontologies-to-improve-performance-in","title":"Using Ontologies To Improve Performance In Massively Multi-label Prediction","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"early-action-prediction-with-generative","title":"Early Action Prediction with Generative Adversarial Networks","date":"2019-04-30","arxiv_id":"1904.13085","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-deep-learning-models-for","title":"Sequence to sequence deep learning models for solar irradiation forecasting","date":"2019-04-30","arxiv_id":"1904.13081","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-prediction-using-cgans-with-fusion-1","title":"Structured Prediction using cGANs with Fusion Discriminator","date":"2019-04-30","arxiv_id":"1904.13358","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-with-unpredictable-feature","title":"Prediction with Unpredictable Feature Evolution","date":"2019-04-27","arxiv_id":"1904.12171","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-with-expert-advice-a-pde","title":"Prediction with Expert Advice: a PDE Perspective","date":"2019-04-25","arxiv_id":"1904.11401","repositories_listed":0,"syntology":null},{"url":null,"slug":"190412040","title":"Community Detection and Growth Potential Prediction from Patent Citation Networks","date":"2019-04-23","arxiv_id":"1904.12040","repositories_listed":0,"syntology":null},{"url":null,"slug":"cpm-sensitive-auc-for-ctr-prediction","title":"CPM-sensitive AUC for CTR prediction","date":"2019-04-23","arxiv_id":"1904.10272","repositories_listed":0,"syntology":null},{"url":null,"slug":"crop-yield-probability-density-forecasting","title":"Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function","date":"2019-04-23","arxiv_id":"1904.10959","repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-in-multiplex-networks-based","title":"Link Prediction in Multiplex Networks based on Interlayer Similarity","date":"2019-04-23","arxiv_id":"1904.10169","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-a-prediction-in-some-nonlinear","title":"Explaining a prediction in some nonlinear models","date":"2019-04-21","arxiv_id":"1904.09615","repositories_listed":0,"syntology":null},{"url":null,"slug":"190409405","title":"FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition","date":"2019-04-20","arxiv_id":"1904.09405","repositories_listed":0,"syntology":null},{"url":null,"slug":"190409412","title":"Cubic LSTMs for Video Prediction","date":"2019-04-20","arxiv_id":"1904.09412","repositories_listed":0,"syntology":null},{"url":null,"slug":"specification-driven-predictive-business","title":"Specification-Driven Predictive Business Process Monitoring","date":"2019-04-20","arxiv_id":"1904.09422","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-multi-label-classification","title":"Reliable Multi-label Classification: Prediction with Partial Abstention","date":"2019-04-19","arxiv_id":"1904.09235","repositories_listed":0,"syntology":null},{"url":null,"slug":"190500752","title":"Hybrid Mortality Prediction using Multiple Source Systems","date":"2019-04-18","arxiv_id":"1905.00752","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-is-a-prediction-knowledge","title":"When is a Prediction Knowledge?","date":"2019-04-18","arxiv_id":"1904.09024","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-accuracy-prediction-for-amr-parsing","title":"Automatic Accuracy Prediction for AMR Parsing","date":"2019-04-17","arxiv_id":"1904.08301","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-drug-target-interaction-using-3d","title":"Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks","date":"2019-04-17","arxiv_id":"1904.08144","repositories_listed":0,"syntology":null},{"url":null,"slug":"dstp-rnn-a-dual-stage-two-phase-attention","title":"DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction","date":"2019-04-16","arxiv_id":"1904.07464","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-event-log-data-attributes-in-rnn","title":"Exploiting Event Log Event Attributes in RNN Based Prediction","date":"2019-04-15","arxiv_id":"1904.06895","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-method-for-anomaly-detection-in","title":"Graph-Based Method for Anomaly Prediction in Brain Network","date":"2019-04-15","arxiv_id":"1904.07163","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-widely-can-prediction-models-be","title":"How Widely Can Prediction Models be Generalized? Performance Prediction in Blended Courses","date":"2019-04-15","arxiv_id":"1904.07328","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-prediction-based-adaptive","title":"A Neural Network Prediction Based Adaptive Mode Selection Scheme in Full-Duplex Cognitive Networks","date":"2019-04-12","arxiv_id":"1904.06222","repositories_listed":0,"syntology":null},{"url":null,"slug":"digging-deeper-into-egocentric-gaze","title":"Digging Deeper into Egocentric Gaze Prediction","date":"2019-04-12","arxiv_id":"1904.06090","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-aware-convolutional-networks-for","title":"Position-Aware Convolutional Networks for Traffic Prediction","date":"2019-04-12","arxiv_id":"1904.06187","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-prediction-errors-for-deep-neural","title":"Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout","date":"2019-04-12","arxiv_id":"1904.06330","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-rnn-sequence-prediction-model-to","title":"Adapting RNN Sequence Prediction Model to Multi-label Set Prediction","date":"2019-04-11","arxiv_id":"1904.05829","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-signaling-proteins-based-on","title":"Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models","date":"2019-04-10","arxiv_id":"1904.05052","repositories_listed":0,"syntology":null},{"url":null,"slug":"next-active-object-prediction-from-egocentric","title":"Next-Active-Object prediction from Egocentric Videos","date":"2019-04-10","arxiv_id":"1904.05250","repositories_listed":0,"syntology":null},{"url":null,"slug":"generic-variance-bounds-on-estimation-and","title":"Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective","date":"2019-04-09","arxiv_id":"1904.04765","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultrafast-video-attention-prediction-with","title":"Ultrafast Video Attention Prediction with Coupled Knowledge Distillation","date":"2019-04-09","arxiv_id":"1904.04449","repositories_listed":0,"syntology":null},{"url":null,"slug":"component-wise-boosting-of-targets-for-multi","title":"Component-Wise Boosting of Targets for Multi-Output Prediction","date":"2019-04-08","arxiv_id":"1904.03943","repositories_listed":0,"syntology":null},{"url":"/paper/human-intracranial-eeg-quantitative-analysis","slug":"human-intracranial-eeg-quantitative-analysis","title":"Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction","date":"2019-04-07","arxiv_id":"1904.03603","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-human-motion-prediction","title":"Context-aware Human Motion Prediction","date":"2019-04-06","arxiv_id":"1904.03419","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-predictive-video-compression-with-bi","title":"Deep Predictive Video Compression with Bi-directional Prediction","date":"2019-04-05","arxiv_id":"1904.02909","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-tracking-segmentation","title":"Prediction-Tracking-Segmentation","date":"2019-04-05","arxiv_id":"1904.03280","repositories_listed":0,"syntology":null},{"url":null,"slug":"composition-of-sentence-embeddingslessons","title":"Composition of Sentence Embeddings:Lessons from Statistical Relational Learning","date":"2019-04-04","arxiv_id":"1904.02464","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-aware-probabilistic-trajectory","title":"Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification","date":"2019-04-04","arxiv_id":"1904.02765","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-landscape-features-for-improving-vector","title":"Deep Landscape Features for Improving Vector-borne Disease Prediction","date":"2019-04-03","arxiv_id":"1904.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-chinese-word-usage","title":"Multi-task Learning for Chinese Word Usage Errors Detection","date":"2019-04-03","arxiv_id":"1904.01783","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-multi-agent-counterfactual-prediction","title":"Robust Multi-agent Counterfactual Prediction","date":"2019-04-03","arxiv_id":"1904.02235","repositories_listed":0,"syntology":null},{"url":null,"slug":"barista-efficient-and-scalable-serverless","title":"BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services","date":"2019-04-02","arxiv_id":"1904.01576","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsal-gan-denoising-based-saliency-prediction","title":"DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks","date":"2019-04-02","arxiv_id":"1904.01215","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-sparse-classifier-for-adolescent","title":"Multimodal Sparse Classifier for Adolescent Brain Age Prediction","date":"2019-04-01","arxiv_id":"1904.01070","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-phase-flow-regime-prediction-using-lstm","title":"Two-phase flow regime prediction using LSTM based deep recurrent neural network","date":"2019-03-30","arxiv_id":"1904.00291","repositories_listed":0,"syntology":null},{"url":null,"slug":"thyroid-cancer-malignancy-prediction-from","title":"Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images","date":"2019-03-29","arxiv_id":"1904.00839","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-state-space-models-unsupervised","title":"DISENTANGLED STATE SPACE MODELS: UNSUPERVISED LEARNING OF DYNAMICS ACROSS HETEROGENEOUS ENVIRONMENTS","date":"2019-03-27","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"652b01192cd40e9c10f456a64146b7defb53d8ce02e4aea3c3e64309e3a37223","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}