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Found 9 papers, 4 papers with code
Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors -- Algorithm and Application
Although real-time analysis is necessary in the dual-way streaming, static PARAFAC2 decomposition methods fail to efficiently work in this setting since they perform PARAFAC2 decomposition for accumulated tensors whenever new data arrive.
Accurate Open-set Recognition for Memory Workload
How can we accurately identify new memory workloads while classifying known memory workloads?
Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching.
DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors
In this paper, we propose DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense tensors.
Time-Aware Tensor Decomposition for Missing Entry Prediction
The essential problems of how to exploit the temporal property for tensor decomposition and consider the sparsity of time slices remain unresolved.
Fast Partial Fourier Transform
In this paper, we propose a fast Partial Fourier Transform (PFT), a careful modification of the Cooley-Tukey algorithm that enables one to specify an arbitrary consecutive range where the coefficients should be computed.
FALCON: Fast and Lightweight Convolution for Compressing and Accelerating CNN
A promising direction is based on depthwise separable convolution which replaces a standard convolution with a depthwise convolution and a pointwise convolution.
FALCON: Lightweight and Accurate Convolution
By exploiting the knowledge of a trained standard model and carefully determining the order of depthwise separable convolution via GEP, FALCON achieves sufficient accuracy close to that of the trained standard model.
VeST: Very Sparse Tucker Factorization of Large-Scale Tensors
Given a large tensor, how can we decompose it to sparse core tensor and factor matrices such that it is easier to interpret the results?
Numerical Analysis
