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11
Neural Network Verification for Nonlinear Systems.
Neural Network Verification for Nonlinear Systems.
Sidrane, Chelsea Rose. | Stanford University. | 2022
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12
Optimization in the Space of Measures: New Techniques from Optimal Transport.
Optimization in the Space of Measures: New Techniques from Optimal Transport.
Kent, Carson Richard. | Stanford University. | 2021
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13
Truncation Algorithms for Markov Chains and Processes.
Truncation Algorithms for Markov Chains and Processes.
Infanger, Alexander GerdDara. | Stanford University. | 2022
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14
Fundamental Limits of Caching: Symmetry Structure and Coded Placement Schemes.
Fundamental Limits of Caching: Symmetry Structure and Coded Placement Schemes.
Zhang, Kai.Electrical and Computer Engineering. | Texas A&M University. | 2020
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15
Control, Filtering, Learning, and Multi-Robot Algorithms for Large Graph-Based Markov Decision Processes.
Control, Filtering, Learning, and Multi-Robot Algorithms for Large Graph-Based Markov Decision Processes.
Haksar, Ravi N. | Stanford University. | 2020
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16
Uniqueness for Continuous Superresolution by Means of Choquet Theory and Geometric Measure Theory.
Uniqueness for Continuous Superresolution by Means of Choquet Theory and Geometric Measure Theory.
Cinoman, Ryan M.Mathematics. | University of Maryland, College Park. | 2021
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17
Safety Enhanced Designs in UAS Risk Monitoring and Collision Resolution.
Safety Enhanced Designs in UAS Risk Monitoring and Collision Resolution.
Wang, Weichang.Electrical Engineering. | Arizona State University. | 2021
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18
Designing Scheduling Algorithms via a Mathematical Perspective.
Designing Scheduling Algorithms via a Mathematical Perspective.
Davies, Sami.Mathematics. | University of Washington. | 2021
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19
Mathematical Modeling in Dairy Cattle - Nonlinear Ration Formulation, Thermal Balance, Emission and Excretion Models.
Mathematical Modeling in Dairy Cattle - Nonlinear Ration Formulation, Thermal Balance, Emission and Excretion Models.
Li, Jinghui.Animal Biology. | University of California, Davis. | 2021
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20
Making Bayesian Optimization Practical in the Context of High Dimensional, Highly Expensive, Black­Box Functions.
Making Bayesian Optimization Practical in the Context of High Dimensional, Highly Expensive, Black­Box Functions.
Mathesen, Logan.Industrial Engineering. | Arizona State University. | 2021
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