Results 11 to 20 of about 1,606,312 (290)
Learning Multiple Defaults for Machine Learning Algorithms [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new ...
Bischl, Bernd +4 more
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Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation
Mathematics, 2022 The quantum approximate optimisation algorithm is a p layer, time variable split operator method executed on a quantum processor and driven to convergence by classical outer-loop optimisation.
Daniil Rabinovich +4 more
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Communications Biology, 2021 Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al.
Iason Batzianoulis +6 more
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Learning Optimal Fin-Ray Finger Design for Soft Grasping
Frontiers in Robotics and AI, 2021 The development of soft hands is an important progress to empower robotic grasping with passive compliance while greatly decreasing the complexity of control. Despite the advances during the past decades, it is still not clear how to design optimal hands
Zhifeng Deng, Miao Li, Miao Li
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ALGORITHMIC TRADING WITH LEARNING [PDF]
International Journal of Theoretical and Applied Finance, 2013 We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of their predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates their predictions to tweak their strategy.
Cartea, A, Jaimungal, S, Kinzebulatov, D
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BMC Ecology and Evolution, 2021 Background The SARS-CoV-2 pandemic is one of the greatest global medical and social challenges that have emerged in recent history. Human coronavirus strains discovered during previous SARS outbreaks have been hypothesized to pass from bats to humans ...
Vladimir Makarenkov +3 more
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The dropout learning algorithm [PDF]
Artificial Intelligence, 2014 Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable ...
Pierre Baldi, Peter J. Sadowski
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ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget
IoT, 2021 Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of
Guillaume Coiffier +2 more
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Scientific Reports, 2023 During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin.
Fumiaki Iwane +2 more
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Forgetting Enhances Episodic Control With Structured Memories
Frontiers in Computational Neuroscience, 2022 Forgetting is a normal process in healthy brains, and evidence suggests that the mammalian brain forgets more than is required based on limitations of mnemonic capacity. Episodic memories, in particular, are liable to be forgotten over time.
Annik Yalnizyan-Carson +7 more
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