Search Results for author:
Found 9 papers, 5 papers with code
Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical characteristic of resource-limited applications.
Simple and efficient algorithms for training machine learning potentials to force data
These forces provide much more information than the energy alone.
Automated discovery of a robust interatomic potential for aluminum
The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset.
Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e. g. indoor, underground.
Robotics
Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion
As a first pass in this direction, we equip a wireless, monocular color camera to the head of a robotic snake.
Less is more: sampling chemical space with active learning
In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials.
Hierarchical modeling of molecular energies using a deep neural network
We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations.
Ranked #13 on
Formation Energy
on QM9
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy.
ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition.
Chemical Physics
