IEEE

Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning

Abstract: This study investigates the design of reward functions for deep reinforcement learning-based source term estimation (STE). Estimating the properties of unknown hazardous gas leakage using a ...

An AI-driven strategy to accelerate microbial gene function discovery

We know the genes, but not their functions—to resolve this long-standing bottleneck in microbial research, a joint research ...
EurekAlert!

KAIST proposes AI-driven strategy to solve long-standing mystery of gene function

We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research ...
Frontiers

Modeling, Machine Learning, Bioinformatics and Multi-omics Data in Soil Science

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...
Government Technology

Using Machine Learning to Understand College Closures

Researchers worked with the Federal Reserve to create a predictive model that assesses hundreds of institutional ...

Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
Hosted on MSN

Learning functions made easy

Hundreds of residents signed up for FEMA buyouts after Helene. Not one has been approved. Three Americans killed — Trump vows 'serious' retaliation Patrick Mahomes shares joyous Christmas photo in leg ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...