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Found 13 papers, 2 papers with code
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics
The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks.
Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence
We endeavor to design an efficient and provable method for expensive POCOPs under the framework of constrained Bayesian optimization.
On the Foundation of Distributionally Robust Reinforcement Learning
This is accomplished through a comprehensive modeling framework centered around distributionally robust Markov decision processes (DRMDPs).
Optimal Sample Complexity for Average Reward Markov Decision Processes
In this context, the existing literature provides a sample complexity upper bound of $\widetilde O(|S||A|t_{\text{mix}}^2 \epsilon^{-2})$ and a lower bound of $\Omega(|S||A|t_{\text{mix}} \epsilon^{-2})$.
Intelligent machines work in unstructured environments by differential neuromorphic computing
By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer.
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Further, the variance-reduced distributionally robust Q-learning combines the synchronous Q-learning with variance-reduction techniques to enhance its performance.
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
We consider a reinforcement learning setting in which the deployment environment is different from the training environment.
Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes
We consider the optimal sample complexity theory of tabular reinforcement learning (RL) for maximizing the infinite horizon discounted reward in a Markov decision process (MDP).
Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization
This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using the more efficient online algorithms through time-varying optimization.
Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation
On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study.
Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation
This paper introduces this method to tracking problem for heterogeneous linear systems.
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy.
