2,095 research outputs found
Multi-Fidelity Active Learning with GFlowNets
In the last decades, the capacity to generate large amounts of data in
science and engineering applications has been growing steadily. Meanwhile, the
progress in machine learning has turned it into a suitable tool to process and
utilise the available data. Nonetheless, many relevant scientific and
engineering problems present challenges where current machine learning methods
cannot yet efficiently leverage the available data and resources. For example,
in scientific discovery, we are often faced with the problem of exploring very
large, high-dimensional spaces, where querying a high fidelity, black-box
objective function is very expensive. Progress in machine learning methods that
can efficiently tackle such problems would help accelerate currently crucial
areas such as drug and materials discovery. In this paper, we propose the use
of GFlowNets for multi-fidelity active learning, where multiple approximations
of the black-box function are available at lower fidelity and cost. GFlowNets
are recently proposed methods for amortised probabilistic inference that have
proven efficient for exploring large, high-dimensional spaces and can hence be
practical in the multi-fidelity setting too. Here, we describe our algorithm
for multi-fidelity active learning with GFlowNets and evaluate its performance
in both well-studied synthetic tasks and practically relevant applications of
molecular discovery. Our results show that multi-fidelity active learning with
GFlowNets can efficiently leverage the availability of multiple oracles with
different costs and fidelities to accelerate scientific discovery and
engineering design.Comment: Code: https://github.com/nikita-0209/mf-al-gf
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain
(FDTD) simulations to optimize transmission properties of plasmonic mirror
color filters using a multi-objective figure of merit over a five-dimensional
parameter space by utilizing novel multi-fidelity Gaussian processes approach.
We compare these results with conventional derivative-free global search
algorithms, such as (single-fidelity) Gaussian Processes optimization scheme,
and Particle Swarm Optimization---a commonly used method in nanophotonics
community, which is implemented in Lumerical commercial photonics software. We
demonstrate the performance of various numerical optimization approaches on
several pre-collected real-world datasets and show that by properly trading off
expensive information sources with cheap simulations, one can more effectively
optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
arXiv admin note: substantial text overlap with arXiv:1811.0075
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