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Found 12 papers, 6 papers with code
Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias
Alzheimer's disease is a progressive, debilitating neurodegenerative disease that affects 50 million people globally.
Learning from Integral Losses in Physics Informed Neural Networks
Our numerical results confirm the existence of the aforementioned bias in practice, and also show that our proposed delayed target approach can lead to accurate solutions with comparable quality to ones estimated with a large number of samples.
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching
Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity.
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants.
Truly Deterministic Policy Optimization
In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape.
Learning Interface Conditions in Domain Decomposition Solvers
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations.
Efficient Feedback and Partial Credit Grading for Proof Blocks Problems
In this work, we propose an algorithm for the edit distance problem that significantly outperforms the baseline procedure of exhaustively enumerating over the entire search space.
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
A system of linear equations defines a graph on the set of unknowns and each level of a multigrid solver requires the selection of an appropriate coarse graph along with restriction and interpolation operators that map to and from the coarse representation.
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision
We demonstrate a successful navigation and docking control system for the John Deere Tango autonomous mower, using only a single camera as the input.
Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale
Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices.
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety.
A tree-based radial basis function method for noisy parallel surrogate optimization
Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems.
