Results 171 to 180 of about 1,516 (207)

Bias-Reduced Localization for Drone Swarm Based on Sensor Selection. [PDF]

Sensors (Basel)
Wu B, Shen B, Zhang Y, Yang L, Wang Z.
europepmc   +1 more source

Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal. [PDF]

Brain Sci
Iqbal Z, Rahman MM, Mahmood U, Zia Q, Fu Z, Calhoun VD, Plis S.   +6 more
europepmc   +1 more source

Explainable AI in early autism detection: a literature review of interpretable machine learning approaches. [PDF]

Discov Ment Health
Agrawal R, Agrawal R.
europepmc   +1 more source

A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification. [PDF]

Sci Rep
Sangeetha V, Agilandeeswari L.
europepmc   +1 more source

Submodular Optimization: Variants, Theory and Applications

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
openaire   +1 more source

Multiobject Tracking by Submodular Optimization [PDF]

IEEE Transactions on Cybernetics, 2019
In 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.
Dacheng Tao, Hanqiu Sun, Jianbing Shen   +2 more
exaly   +4 more sources

Submodular Functions: Learnability, Structure, and Optimization [PDF]

SIAM Journal on Computing, 2018
Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we study submodular functions from a learning theoretic angle.
Maria-Florina Balcan
exaly   +3 more sources

Submodular optimization problems and greedy strategies: A survey [PDF]

Discrete Event Dynamic Systems: Theory and Applications, 2020
The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the optimal solution? In this survey, we mainly consider two classes of optimization problems where the objective function is submodular. The first is set submodular optimization,
Yajing Liu, Ali Pezeshki, Edwin K P Chong   +2 more
exaly   +3 more sources

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Temporal Biased Streaming Submodular Optimization

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Submodular 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, Pinghui Wang, Chao Deng, Jing Tao   +3 more
openaire   +1 more source