HydroGraphNet boosts watershed predictions of daily flow and nitrogen in sparse data regions

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...
Frontiers

Adaptive multi-mode locomotion for bipedal wheel-legged robots via sparse mixture-of-experts deep reinforcement learning

The bipedal wheel-legged robot combines the high energy efficiency of wheeled movement with the terrain adaptability of legged locomotion. However, achieving a smooth transition between these two ...
IEEE

Gaussian Process-Based Learning Model Predictive Control With Application to Flywheel Battery System

Abstract: The flywheel battery system is extremely sensitive to its own time-varying nonlinear characteristics and random disturbances in actual operating conditions. The traditional model predictive ...
IEEE

Robot Impedance Iterative Learning with Sparse Online Gaussian Process

Abstract: Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments. Iterative learning (IL) is effective to ...
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 ...
Physicians Committee

Human-Based Brain Organoids Replicate Key Processes of Learning and Memory

Study in a Sentence: Brain organoids derived from human cells demonstrate complex neuronal networks that can mimic the genetic activity linked to learning and memory. Healthy for Humans: The human ...
GitHub

deep-kernel-learning

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood ...
GitHub

deep-kernel-learning

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...