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Comparative analysis of deep Q-learning algorithms for object throwing using a robot manipulator. [PDF]
Front Robot AIAl Homsi M +3 more
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Learning to Learn Variational Quantum Algorithm
IEEE Transactions on Neural Networks and Learning Systems, 2023Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called "meta-learner." Motivated by the hybrid quantum ...
Rui Huang, Xiaoqing Tan, Qingshan Xu
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Online Pairwise Learning Algorithms
Neural Computation, 2016Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing
Ying, Yiming, Zhou, Ding-Xuan
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'Un'Fair Machine Learning Algorithms
SSRN Electronic Journal, 2019Ensuring fairness in algorithmic decision making is a crucial policy issue. Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision making. As a result, to be legally compliant, the algorithms need to ensure equal treatment.
Runshan Fu +3 more
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Perceptron-based learning algorithms
IEEE Transactions on Neural Networks, 1990A key task for connectionist research is the development and analysis of learning algorithms. An examination is made of several supervised learning algorithms for single-cell and network models. The heart of these algorithms is the pocket algorithm, a modification of perceptron learning that makes perceptron learning well-behaved with nonseparable ...
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2019
Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and ...
Namrata Dhanda +2 more
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Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and ...
Namrata Dhanda +2 more
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Teaching Algorithms and Learning Algorithms
Programmed Learning and Educational Technology, 1982Abstract *A11 teaching processes can be precisely specified by means of Helmar Frank's six didactic variables (each of which can in turn be interpreted as a vector of vectors). These are: learning system, teaching system, subject matter, target standard, environment and teaching algorithm.
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2020
In this chapter, we introduce the Algorithmic Deep Learning Neural Network (ADLNN), a deep learning system that incorporates algorithmic descriptions of the processes as part of the deep learning neural network. The dynamical models provide domain knowledge. These are in the form of differential equations.
Michael Paluszek, Stephanie Thomas
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In this chapter, we introduce the Algorithmic Deep Learning Neural Network (ADLNN), a deep learning system that incorporates algorithmic descriptions of the processes as part of the deep learning neural network. The dynamical models provide domain knowledge. These are in the form of differential equations.
Michael Paluszek, Stephanie Thomas
openaire +1 more source