4. In Sect. 6 we show results for all baselines on the physics test problems where the objective and constraints have been transformed in the same fashion as in SCBO. Finally, Sect. 7 provides details on all benchmarks.David ErikssonMatthias PoloczekPMLR...
25, which also adaptively collects information while taking into account an internal model’s uncertainty of possible experiments. Unlike active learning, whose goal is to model the entire experimental space (perhaps subject to some resolution), Bayesian optimization seeks to find a single optimizer of...
To perform HPO at large scale, we used DeepHyper [80], an open-source Python package designed for optimizing hyperparameters, searching for optimal neural architectures. Specifically, we used asynchronous Bayesian optimization that continuously refines a surrogate model by sampling hyperparameter configur...
CUTE: constrained and unconstrained testing environment The purpose of this article is to discuss the scope and functionality of a versatile environment for testing small- and large-scale nonlinear optimization ... I Bongartz,AR Conn,N Gould,... - 《Acm Transactions on Mathematical Software》 被...
S. Bandwidth-constrained decentralized detection of an unknown vector signal via multisensor fusion. IEEE Transactions on Signal and Information Processing over Networks 6, 744–758 (2020). 6. Li, C., Li, G. & Varshney, P. K. Distributed detection of sparse stochastic signals with 1-bit ...
In contrast, strategies for constrained SP-CET bypass the need to extract sub-tomograms and only use two-dimensional (2D) projections during refinement, resulting in substantial storage savings34. In practice, however, even this solution may require unreasonable amounts of space and create input/...
We show how to approximate a data matrix $mathbf{A}$ with a much smaller sketch $mathbf{ ilde A}$ that can be used to solve a general class of constrained k-rank approximation problems to within $(1+epsilon)$ error. Importantly, this class of problems includes $k$-means clustering and...
cross-scale sensing (Section 3.1) provides high-resolution and spatially-explicit E, M, C observations, which are then used as either inputs or constraints for a model with necessary processes represented (Section 3.2), and a set of location-specific parameters will be constrained for every fiel...
In addition, DL methods are constrained by their datasets, causing decreased accuracy when datasets are of insufficient size or comprise imbalanced samples. Current research on the classification of encrypted Internet traffic services is mainly based on the ISCX VPN-nonVPN dataset published in 2016 [...
Management of Networks with Constrained Devices: Use Cases. IETF Internet Draft. https://goo.gl/cT5pXr. Accessed 15 Mar 2016 Mobile-edge computing - introductory technical white paper. https://goo.gl/ybrCnq. Accessed 15 Mar 2016 The Internet of Things: how the next evolution of the Internet...