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Autor: Amin, Ahmad Ayaz

Natural evolution strategies are a class of approximate-gradient black-box optimizers that have been successfully used for continuous parameter spaces. In this paper, we derive NES algorithms for discrete parameter spaces and demonstrate their effect

Externí odkaz: http://arxiv.org/abs/2404.00208

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Guided Sketch-Based Program Induction by Search Gradients

Autor: Amin, Ahmad Ayaz

Many tasks can be easily solved using machine learning techniques. However, some tasks cannot readily be solved using statistical models, requiring a symbolic approach instead. Program induction is one of the ways that such tasks can be solved by mea

Externí odkaz: http://arxiv.org/abs/2402.06990

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Efficient UAVs Deployment and Resource Allocation in UAV-Relay Assisted Public Safety Networks for Video Transmission

Autor: Khan, Naveed, Ahmad, Ayaz, Wakeel, Abdul, Kaleem, Zeeshan, Rashid, Bushra, Khalid, Waqas

Wireless communication highly depends on the cellular ground base station (GBS). A failure of the cellular GBS, fully or partially, during natural or man-made disasters creates a communication gap in the disaster-affected areas. In such situations, p

Externí odkaz: http://arxiv.org/abs/2401.01636

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Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference

Autor: Amin, Ahmad Ayaz

Stochastic variational inference and its derivatives in the form of variational autoencoders enjoy the ability to perform Bayesian inference on large datasets in an efficient manner. However, performing inference with a VAE requires a certain design

Externí odkaz: http://arxiv.org/abs/2308.08053

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