38 research outputs found
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Bayesian optimization has attracted huge attention from diverse research
areas in science and engineering, since it is capable of finding a global
optimum of an expensive-to-evaluate black-box function efficiently. In general,
a probabilistic regression model, e.g., Gaussian processes and Bayesian neural
networks, is widely used as a surrogate function to model an explicit
distribution over function evaluations given an input to estimate and a
training dataset. Beyond the probabilistic regression-based Bayesian
optimization, density ratio estimation-based Bayesian optimization has been
suggested in order to estimate a density ratio of the groups relatively close
and relatively far to a global optimum. Developing this line of research
further, a supervised classifier can be employed to estimate a class
probability for the two groups instead of a density ratio. However, the
supervised classifiers used in this strategy are prone to be overconfident for
a global solution candidate. To solve this problem, we propose density ratio
estimation-based Bayesian optimization with semi-supervised learning. Finally,
we demonstrate the experimental results of our methods and several baseline
methods in two distinct scenarios with unlabeled point sampling and a
fixed-size pool.Comment: 20 pages, 14 figures, 2 table
Generative Neural Fields by Mixtures of Neural Implicit Functions
We propose a novel approach to learning the generative neural fields
represented by linear combinations of implicit basis networks. Our algorithm
learns basis networks in the form of implicit neural representations and their
coefficients in a latent space by either conducting meta-learning or adopting
auto-decoding paradigms. The proposed method easily enlarges the capacity of
generative neural fields by increasing the number of basis networks while
maintaining the size of a network for inference to be small through their
weighted model averaging. Consequently, sampling instances using the model is
efficient in terms of latency and memory footprint. Moreover, we customize
denoising diffusion probabilistic model for a target task to sample latent
mixture coefficients, which allows our final model to generate unseen data
effectively. Experiments show that our approach achieves competitive generation
performance on diverse benchmarks for images, voxel data, and NeRF scenes
without sophisticated designs for specific modalities and domains
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope
Bayesian optimization is a popular method for solving the problem of global
optimization of an expensive-to-evaluate black-box function. It relies on a
probabilistic surrogate model of the objective function, upon which an
acquisition function is built to determine where next to evaluate the objective
function. In general, Bayesian optimization with Gaussian process regression
operates on a continuous space. When input variables are categorical or
discrete, an extra care is needed. A common approach is to use one-hot encoded
or Boolean representation for categorical variables which might yield a {\em
combinatorial explosion} problem. In this paper we present a method for
Bayesian optimization in a combinatorial space, which can operate well in a
large combinatorial space. The main idea is to use a random mapping which
embeds the combinatorial space into a convex polytope in a continuous space, on
which all essential process is performed to determine a solution to the
black-box optimization in the combinatorial space. We describe our {\em
combinatorial Bayesian optimization} algorithm and present its regret analysis.
Numerical experiments demonstrate that our method outperforms existing methods.Comment: 10 pages, 2 figure
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
Nanophotonic structures have versatile applications including solar cells,
anti-reflective coatings, electromagnetic interference shielding, optical
filters, and light emitting diodes. To design and understand these nanophotonic
structures, electrodynamic simulations are essential. These simulations enable
us to model electromagnetic fields over time and calculate optical properties.
In this work, we introduce frameworks and benchmarks to evaluate nanophotonic
structures in the context of parametric structure design problems. The
benchmarks are instrumental in assessing the performance of optimization
algorithms and identifying an optimal structure based on target optical
properties. Moreover, we explore the impact of varying grid sizes in
electrodynamic simulations, shedding light on how evaluation fidelity can be
strategically leveraged in enhancing structure designs.Comment: 31 pages, 31 figures, 4 tables. Accepted at the 37th Conference on
Neural Information Processing Systems (NeurIPS 2023), Datasets and Benchmarks
Trac
Distribution of magnetic domain pinning fields in GaMnAs ferromagnetic films
Using the angular dependence of the planar Hall effect in GaMnAs
ferromagnetic films, we were able to determine the distribution of magnetic
domain pinning fields in this material. Interestingly, there is a major
difference between the pinning field distribution in as-grown and in annealed
films, the former showing a strikingly narrower distribution than the latter.
This conspicuous difference can be attributed to the degree of non-uniformity
of magnetic anisotropy in both types of films. This finding provides a better
understanding of the magnetic domain landscape in GaMnAs that has been the
subject of intense debate