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Showing 5426–5450 of 9169 papers

TitleStatusHype
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""—0
A Bayesian approach for structure learning in oscillating regulatory networks—0
An investigation of higher order moments of empirical financial data and the implications to risk—0
Modeling Evolution of Message Interaction for Rumor Resolution—0
Modeling Extreme Events in Time Series Prediction—0
Individual Topology Structure of Eye Movement Trajectories—0
Modeling, Inference, and Prediction in Mobility-Based Compartmental Models for Epidemiology—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—0
Modeling joint probability distribution of yield curve parameters—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Domain-invariant Feature Exploration for Domain Generalization—0
Analysis of Distributional Dynamics for Repeated Cross-Sectional and Intra-Period Observations—0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies—0
Modeling non-stationarities in high-frequency financial time series—0
Modeling of Low Rank Time Series—0
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network—0
Modeling of Political Systems using Wasserstein Gradient Flows—0
Modeling of time series using random forests: theoretical developments—0
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units—0
Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health—0
Modeling Regime Shifts in Multiple Time Series—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs—0
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