5 citations · 13 across the 8 of their papers we have counts for
10 papers
Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Hang Zhang, Victor Armegioiu, Juan Carrasquilla +4
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization m…
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Anas Jnini, Elham Kiyani, Khemraj Shukla +5
Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy -- faithfully capturing co…
Non-Asymptotic Analysis of Projected Gradient Descent for Physics-Informed Neural Networks
Jonas Nießen, Johannes Müller
In this work, we provide a non-asymptotic convergence analysis of projected gradient descent for physics-informed neural networks for the Poisson equation. Under suitable assumptio…
Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
Johannes Müller, Semih Cayci
We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the in…
Algebraic optimization of sequential decision problems
Mareike Dressler, Marina Garrote-López, Guido Montúfar +2
We study the optimization of the expected long-term reward in finite partially observable Markov decision processes over the set of stationary stochastic policies. In the case of d…
Geometry and convergence of natural policy gradient methods
Johannes Müller, Guido Montúfar
We study the convergence of several natural policy gradient (NPG) methods in infinite-horizon discounted Markov decision processes with regular policy parametrizations. For a varie…
Extrinsic Camera Calibration with Semantic Segmentation
Alexander Tsaregorodtsev, Johannes Müller, Jan Strohbeck +3
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating th…
Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action space
Johannes Müller, Guido Montúfar
Reward optimization in fully observable Markov decision processes is equivalent to a linear program over the polytope of state-action frequencies. Taking a similar perspective in t…
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs
Johannes Müller, Guido Montúfar
We consider the problem of finding the best memoryless stochastic policy for an infinite-horizon partially observable Markov decision process (POMDP) with finite state and action s…
Subjective Logic-based Identification of Markov Chains and Its Application to CAV's Safety
Johannes Müller, Thomas Griebel, Michael Gabb +1
A reliable estimation of the communication chan-nel which connects automated vehicles is an important steptowards the safety of connected and automated vehicles. The communication…