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Found 7 papers, 0 papers with code
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing
In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states.
Imitation Learning for Agile Autonomous Driving
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors.
Robotics
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Sparse Spectrum Gaussian Processes (SSGPs) are a powerful tool for scaling Gaussian processes (GPs) to large datasets.
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
Robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments.
Learning from Conditional Distributions via Dual Embeddings
In such problems, each sample $x$ itself is associated with a conditional distribution $p(z|x)$ represented by samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links these conditional distributions to target values $y$.
Sample Efficient Path Integral Control under Uncertainty
We present a data-driven stochastic optimal control framework that is derived using the path integral (PI) control approach.
Probabilistic Differential Dynamic Programming
We present a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP).
