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Found 4 papers, 1 papers with code
Neural Network Pruning as Spectrum Preserving Process
In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum.
MLCTR: A Fast Scalable Coupled Tensor Completion Based on Multi-Layer Non-Linear Matrix Factorization
Firms earning prediction plays a vital role in investment decisions, dividends expectation, and share price.
Enhancing Domain Word Embedding via Latent Semantic Imputation
We present a novel method named Latent Semantic Imputation (LSI) to transfer external knowledge into semantic space for enhancing word embedding.
X-ray Scattering Image Classification Using Deep Learning
In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images.
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