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Found 6 papers, 2 papers with code
Bridging the Sim-to-Real Gap with Bayesian Inference
We present SIM-FSVGD for learning robot dynamics from data.
Active Few-Shot Fine-Tuning
We study the active few-shot fine-tuning of large neural networks to downstream tasks.
Information-based Transductive Active Learning
We generalize active learning to address real-world settings where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region.
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
Differential equations in general and neural ODEs in particular are an essential technique in continuous-time system identification.
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure.
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
The principal task to control dynamical systems is to ensure their stability.
