Third, the framework is formulated as the canonical form of high-order matrix factorizations and then an efficient convergent iterative algorithm is proposed for the problem. Besides, the proposed framework is further extended to multi-view feature selection and fusion problems from an algorithmic view...
we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and ...
Conversely, matrix factorizations incorporate explicit interpretability, as one can try to connect the inferred latent factors to specific user and item features. One example is the factori- zation machine (FM) [12], which combines a linear regression-like term and a feature pairwise interaction ...
which can be computed as the second power of the adjacency matrix,A2, we obtained an AUC that is sometimes higher than preferential attachment and sometimes lower than it but is still consistently quite close with the best learning-based models. ...
A survey on deep matrix factorizations Comp. Sci. Rev., 42 (2021), Article 100423 View PDFView articleView in ScopusGoogle Scholar [20] M. Jamali, M. Ester, A matrix factorization technique with trust propagation for recommendation in social networks, in: Proceedings of the Fourth ACM Confere...
It allows to easily leverage tensor methods in a deep learning setting and comes with all batteries included. Website: http://tensorly.org/torch/ Source-code: https://github.com/tensorly/torch With TensorLy-Torch, you can easily: Tensor Factorizations: decomposing, manipulating and initializing ...
matrix/low-rank decomposition knowledge distillation (KD) Note, this repo is more about pruning (with lottery ticket hypothesis or LTH as a sub-topic), KD, and quantization. For other topics like NAS, see more comprehensive collections (## Related Repos and Websites) at the end of this fil...
Useful in this part are the comparisons of calculating the methods from scratch or with NumPy and with the scikit-learn library. Topic modeling is a great way to get started with matrix factorizations. The topics covered in this lecture are: ...
Fig. 2: Quantum process tomography with tensor networks. a The quantum process (N = 4) is represented by a Choi matrix Λϑ, parametrized by a locally-purified density operator (LPDO). The input and output indices of the process are {σj} and {τj}, respectively. b Tensor contr...
An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized ...