Apr 14, 2020 · We propose an algorithm which integrates both past and future information at every time step with omniscient attention model.
To address this challenge, we propose an Attention-augmentation Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. We ...
To address this challenge, we propose an Attention-augmentation Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. We ...
Oct 22, 2024 · This paper introduces Long Term Memory network (LTM), which can tackle the exploding and vanishing gradient problems and handles long sequences ...
A novel multi-channel hybrid Long Short-Term Memory (LSTM) neural network for effective acoustic log prediction is proposed, which provides an effective ...
To address this challenge, we propose an Attention-augmentation Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. We ...
enhanced residual attention with bidirectional long short-term memory
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Sep 4, 2024 · Each LSTM in the Bi-LSTM structure consists of “output, input, and forget gates”. These three gates are responsible for the updation and ...
Nov 22, 2024 · The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and ...
To this end, our proposed LP strategy exploits pixel-level class positional information to update the multi-label of the augmented training image. We ...
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Attention augmentation with multi-residual in bidirectional LSTM. Y Wang, X Zhang, M Lu, H Wang, Y Choe. Neurocomputing 385, 340-347, 2020. 36, 2020.