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on 15-Oct-2025 (Wed)

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Annotation 7763229543692

#RNN #ariadne #behaviour #consumer #deep-learning #priority #retail #simulation #synthetic-data
Deep Neural Network Ensembles for Time Series Classification [8]. In this paper, authors show how an ensemble of multiple Convolutional Neural Networks can improve upon the state-of-the-art performance of individual neural networks. They use 6 deep learning classifiers including Multi-Layer Perceptron, Fully Convolutional Neural Network, Residual Network, Encoder [20], Multi-Channels Deep Convolutional Neural Networks [29] and Time Convolutional Neural Network [28].
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From Neural Network architectures perspective, close to our work is Deep Neural Network Ensembles for Time Series Classification [8]. In this paper, authors show how an ensemble of multiple Convolutional Neural Networks can improve upon the state-of-the-art performance of individual neural networks. They use 6 deep learning classifiers including Multi-Layer Perceptron, Fully Convolutional Neural Network, Residual Network, Encoder [20], Multi-Channels Deep Convolutional Neural Networks [29] and Time Convolutional Neural Network [28]. The first three were originally proposed in [24]. We propose the application of such architectures in the consumer choice world and apply the concept of entity embeddings [9] along with

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Annotation 7763231116556

#RNN #ariadne #behaviour #consumer #deep-learning #priority #retail #simulation #synthetic-data
We propose the application of such architectures in the consumer choice world and apply the concept of entity embeddings [9] along with neural network architectures like Multi-Layer Perceptron, Long Short Term Memory (LSTM), Temporal Convolutional Networks (TCN) [13] and TCN-LSTM
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ral Network, Residual Network, Encoder [20], Multi-Channels Deep Convolutional Neural Networks [29] and Time Convolutional Neural Network [28]. The first three were originally proposed in [24]. <span>We propose the application of such architectures in the consumer choice world and apply the concept of entity embeddings [9] along with neural network architectures like Multi-Layer Perceptron, Long Short Term Memory (LSTM), Temporal Convolutional Networks (TCN) [13] and TCN-LSTM <span>

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Flashcard 7763233213708

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#deep-learning #keras #lstm #python #sequence
Question
Note that by default, the internal state of the network is [...] after each batch
Answer
reset

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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Note that by default, the internal state of the network is reset after each batch

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