Oct 7, 2017 · In this paper, we propose an ExMaLSTM model to incorporate the explicit matching knowledge into the traditional LSTM neural network. First, we ...
This paper proposes an ExMaLSTM model, which outperforms both the traditional methods and various state-of-the-art neural network models significantly for ...
In this paper, we propose an ExMaLSTM model to incorporate the explicit matching knowledge into the long short-term memory (LSTM) neural network. We extract ...
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Jan 10, 2022 · We introduce a general method to extract knowledge from the latent space based on the clustering of the internal states.
Nov 20, 2021 · This method is illustrated on artificial grammars (Reber grammar variants) as well as on a real use-case in the electrical domain, whose ...
In this paper, we introduce the new ideas of augment- ing Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for ...
Jul 25, 2020 · In this paper, we produce a novel framework (ALSTM) based on the Attention mechanism and Long Short-Term Memory (LSTM), which associates structure learning ...
Unlike these methods, we fully exploit the potential of internal memory of LSTM by adjust- ing its forgetting rates. The other one tries to use multiple time- ...
Matching is a key problem in both search and recommenda- tion, which is to measure the relevance of a document to a query or the interest of a user on an item.
In this work, we examine the advantages of using multiple types of behaviours in recommendation systems. Intuitively, each user often takes some implicit ...