Results 141 to 150 of about 9,788 (160)
Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance. [PDF]
Nat CommunWild R +4 more
europepmc +1 more source
Federated Learning-Oriented Edge Computing Framework for the IIoT. [PDF]
Sensors (Basel)Liu X, Dong X, Jia N, Zhao W.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Multiobject Tracking by Submodular Optimization
IEEE Transactions on Cybernetics, 2019In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem.
Jianbing Shen +5 more
openaire +2 more sources
Temporal Biased Streaming Submodular Optimization
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021Submodular optimization lies at the core of many data mining and machine learning applications such as data summarization and subset selection. For data streams where elements arrive one at a time, streaming submodular optimization (SSO) algorithms are desired.
Junzhou Zhao +3 more
openaire +1 more source
Submodular Optimization with Routing Constraints
Proceedings of the AAAI Conference on Artificial Intelligence, 2016Submodular optimization, particularly under cardinality or cost constraints, has received considerable attention, stemming from its breadth of application, ranging from sensor placement to targeted marketing. However, the constraints faced in many real domains are more complex.
Haifeng Zhang, Yevgeniy Vorobeychik
openaire +1 more source
Centralized Submodular Optimization
2015Submodularity enables efficient approximation of otherwise intractable set optimization problems using simply greedy or local search heuristics, making submodularity a valuable tool in a variety of applications. This chapter gives an overview of submodular optimization algorithms, with emphasis on centralized algorithms for maximizing submodular ...
Andrew Clark +3 more
openaire +1 more source
Submodular Optimization Under Uncertainty
2023Submodular functions, which are a natural discrete analog of convex/concave functions, strike a sweet spot between generality and structure: they model an immense variety of applications in computer science and beyond, but, at the same time, are sufficiently well behaved that they can be optimized very effectively in theory and in practice ...
openaire +1 more source
Minimax Optimal Submodular Optimization with Bandit Feedback
2023We consider maximizing a monotonic, submodular set function $f: 2^{[n]} \rightarrow [0,1]$ under stochastic bandit feedback. Specifically, $f$ is unknown to the learner but at each time $t=1,\dots,T$ the learner chooses a set $S_t \subset [n]$ with $|S_t| \leq k$ and receives reward $f(S_t) + η_t$ where $η_t$ is mean-zero sub-Gaussian noise.
Tajdini, Artin +2 more
openaire +1 more source
Risk-Sensitive Submodular Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018The conditional value at risk (CVaR) is a popular risk measure which enables risk-averse decision making under uncertainty. We consider maximizing the CVaR of a continuous submodular function, an extension of submodular set functions to a continuous domain.
openaire +1 more source
Scenario Reduction With Submodular Optimization
IEEE Transactions on Power Systems, 2017Stochastic programming methods have been proven to deal effectively with the uncertainty and variability of renewable generation resources. However, the quality of the solution that they provide (as measured by cost and reliability metrics) depends on the accuracy and the number of scenarios used to model this uncertainty and variability.
Yishen Wang +2 more
openaire +1 more source