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Found 6 papers, 4 papers with code
Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports
Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports.
Learning Fair Representations for Kernel Models
In contrast, we demonstrate the promise of learning a model-aware fair representation, focusing on kernel-based models.
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation.
Learning Integral Representations of Gaussian Processes
We propose a representation of Gaussian processes (GPs) based on powers of the integral operator defined by a kernel function, we call these stochastic processes integral Gaussian processes (IGPs).
Partitioned Tensor Factorizations for Learning Mixed Membership Models
We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an $O(p^3)$ tensor, to factorizing $O(p/k)$ sub-tensors each of size $O(k^3)$.
Efficient Learning of Mixed Membership Models
We present an efficient algorithm for learning mixed membership models when the number of variables $p$ is much larger than the number of hidden components $k$.
