Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural
Networks (DNN). It offers many significant opportunities for improving DNN performance and …

Factorizing a large matrix into small matrices is a popular strategy for model compression.
Singular value decomposition (SVD) plays a vital role in this compression strategy …

With the boosting of neural networks, recommendation methods become significantly
improved by their powerful ability of prediction and inference. Existing neural-network based …

The appropriate understanding and fast processing of lengthy legal documents are
computationally challenging problems. Designing efficient automatic summarization …

Text has traditionally been used to train automated classifiers for a multitude of purposes,
such as: classification, topic modelling and sentiment analysis. State-of-the-art LSTM …

In the era of deep learning, word embeddings are essential when dealing with text tasks.
However, storing and accessing these embeddings requires a large amount of space. This …

Recurrent neural networks have proved to be an effective method for statistical language
modeling. However, in practice their memory and run-time complexity are usually too large …

Singular value decomposition (SVD) is one of the most popular compression methods that
approximate a target matrix with smaller matrices. However, standard SVD treats the …

This letter focuses on the robust low-rank matrix recovery (RLRMR) in the presence of gross
sparse outliers. Instead of using-norm to reduce or suppress the influence of anomalies, we …

A common technique for compressing a neural network is to compute the k-rank ℓ 2
approximation A k of the matrix A∈ R n× d via SVD that corresponds to a fully connected …