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Found 7 papers, 2 papers with code
Sparse Shrunk Additive Models
Meanwhile, in these feature selection models, the interactions between features are often ignored or just discussed under prior structure information.
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
Our main contributions are that we propose the theorems to characterize the optimal solution of the PWLA problem and present the LNN method for solving it.
How to Explain Neural Networks: an Approximation Perspective
The lack of interpretability has hindered the large-scale adoption of AI technologies.
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem.
