Automated Learning: An Implementation of The A* Search Algorithm over The Random Base Functions
November 09, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nima Tatari
arXiv ID
2211.05085
Category
physics.data-an
Cross-listed
cs.LG
Citations
1
Venue
arXiv.org
Last Checked
3 months ago
Abstract
This letter explains an algorithm for finding a set of base functions. The method aims to capture the leading behavior of the dataset in terms of a few base functions. Implementation of the A-star search will help find these functions, while the gradient descent optimizes the parameters of the functions at each search step. We will show the resulting plots to compare the extrapolation with the unseen data.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.data-an
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
R.I.P.
π»
Ghosted
The Pandora Software Development Kit for Pattern Recognition
R.I.P.
π»
Ghosted
Emergence of Compositional Representations in Restricted Boltzmann Machines
R.I.P.
π»
Ghosted
Investigating echo state networks dynamics by means of recurrence analysis
R.I.P.
π»
Ghosted
Discovering state-parameter mappings in subsurface models using generative adversarial networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted