Search Results for author:
Found 9 papers, 7 papers with code
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap.
Learning physics confers pose-sensitivity in structure-based virtual screening
In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses at specific binding sites.
Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections.
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed.
ATOM3D: Tasks On Molecules in Three Dimensions
We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.
A community-powered search of machine learning strategy space to find NMR property prediction models
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions.
Ranked #1 on
NMR J-coupling
on QM9
Lorentz Group Equivariant Neural Network for Particle Physics
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics.
Cormorant: Covariant Molecular Neural Networks
We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems.
Covariant Compositional Networks For Learning Graphs
Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors.
