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Found 5 papers, 0 papers with code
Memory-Efficient Factorization Machines via Binarizing both Data and Model Coefficients
Our proposed method can significantly reduce the memory cost of SEFM model.
Effective and Sparse Count-Sketch via k-means clustering
This data-oblivious matrix sketching method could produce a bad sketched matrix which will result in low accuracy for subsequent machine learning tasks (e. g. classification); (2) For highly sparse input data, count-sketch could produce a dense sketched data matrix.
Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients
The analysis shows that the convergence to a local optimum is guaranteed, and the inference complexity of our model is much lower than other competing methods.
Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters
One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit.
Improved Subsampled Randomized Hadamard Transform for Linear SVM
To overcome this limitation, we analyze the effect of using SRHT for random projection in the context of linear SVM classification.
