Results 41 to 50 of about 73,328 (265)
Black box optimization for automatic speech recognition [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 State-of-the-art automatic speech recognition (ASR) systems are very complex, combining multiple techniques and involving many types of tuning parameters (e.g., numbers of states and Gaussians in HMMs, numbers of neurons/layers and learning rates in neural networks, etc.).
Shinji Watanabe 0001, Jonathan Le Roux
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The comparison of different PDP-type self-adaptive schemes for the cooperation of GA, DE, and PSO algorithms [PDF]
ITM Web of ConferencesMany global optimization problems are presented as a black-box model, in which there is no information on the objective function properties. Traditional optimization algorithms usually can't effectively solve that kind of problems.
Sopov Anton, Karaseva Tatiana
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Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms
Risks, 2014 This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems.
Sancho Salcedo-Sanz +4 more
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Continuous black-box optimization with an Ising machine and random subspace coding
Physical Review Research, 2022 A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function.
Syun Izawa +4 more
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Sharpness-Aware Black-Box Optimization
CoRRBlack-box optimization algorithms have been widely used in various machine learning problems, including reinforcement learning and prompt fine-tuning. However, directly optimizing the training loss value, as commonly done in existing black-box optimization methods, could lead to suboptimal model quality and generalization performance.
Feiyang Ye 0001 +5 more
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Distributed black-box optimization of nonconvex functions [PDF]
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 We combine model-based methods and distributed stochastic approximation to propose a fully distributed algorithm for nonconvex optimization, with good empirical performance and convergence guarantees. Neither the expression of the objective nor its gradient are known.
Valcarcel Macua, Sergio +2 more
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FEBS Letters, EarlyView.Embryo‐like structures (stembryos) are an innovative tool, but they are hindered by experimental variability and limited developmental potential. DNA methylation is crucial for mammalian development, but its status in stembryo models is poorly characterized.
Sara Canil +4 more
wiley +1 more source
Improving the adversarial transferability with relational graphs ensemble adversarial attack
Frontiers in Neuroscience, 2023 In transferable black-box attacks, adversarial samples remain adversarial across multiple models and are more likely to attack unknown models. From this view, acquiring and exploiting multiple models is the key to improving transferability.
Jiatian Pi +5 more
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Molecular Oncology, EarlyView.Pre‐analytical handling critically determines liquid biopsy performance. This study defines practical best‐practice conditions for cell‐free DNA (cfDNA) and extracellular vesicle–derived DNA (evDNA), showing how processing time, storage conditions, tube type, and plasma input volume affect DNA integrity and mutation detection.
Jonas Dohmen +11 more
wiley +1 more source
Agent-Based Collaborative Random Search for Hyperparameter Tuning and Global Function Optimization
Systems, 2023 Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles ...
Ahmad Esmaeili +2 more
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