37 citations · 55 across the 7 of their papers we have counts for
7 papers
Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan +1
Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool…
Robust Scheduling with GFlowNets
David W. Zhang, Corrado Rainone, Markus Peschl +1
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a s…
Learning Perturbations for Soft-Output Linear MIMO Demappers
Daniel E. Worrall, Markus Peschl, Arash Behboodi +1
Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to th…
Neural Topological Ordering for Computation Graphs
Mukul Gagrani, Corrado Rainone, Yang Yang +7
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In thi…
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
Mathis Gerdes, Pim de Haan, Corrado Rainone +2
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer a…
Neural Simulated Annealing
Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems. Despite its simplicity, the developm…
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
Pim de Haan, Corrado Rainone, Miranda C. N. Cheng +1
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architecture…