JDeskew is a novel skew estimation method that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum.

**NeuroTactic** is a model for theorem proving which leverages [graph neural networks](https://paperswithcode.com/methods/category/graph-models) to represent the theorem and premises, and applies graph contrastive learning for pre-training. Specifically, premise selection is designed as a pretext task for the graph contrastive learning approach. The learned representations are then used for the downstream task, tactic prediction

NeuroTactic

Graph Contrastive Pre-training for Effective Theorem Reasoning

JDeskew

Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation

**Path Planning and Motion Control**, or **PPMC RL**, is a training algorithm that teaches path planning and motion control to robots using reinforcement learning in a simulated environment. The focus is on promoting generalization where there are environmental uncertainties such as rough environments like lunar services. The algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. The algorithm works independently of the robot structure, demonstrating that it works on a wheeled rover in addition to the past results on a quadruped walking robot.

Source	Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com