Search Results for author:
Found 10 papers, 5 papers with code
Stochastic parameter reduced-order model based on hybrid machine learning approaches
Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability.
Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems
The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems.
Multi-task Meta Label Correction for Time Series Prediction
To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.
An end-to-end deep learning approach for extracting stochastic dynamical systems with $α$-stable Lévy noise
Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields.
Generalized Conflict-directed Search for Optimal Ordering Problems
In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning.
BRIDGE: Byzantine-resilient Decentralized Gradient Descent
The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures.
Efficiently Exploring Ordering Problems through Conflict-directed Search
In this paper, we present Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed search method to incrementally and systematically generate event total orders given ordering relations and conflicts returned by sub-solvers.
