Results 141 to 150 of about 9,083 (186)

Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges. [PDF]

Front Pharmacol
Gheorghita FI, Bocanet VI, Iantovics LB.
europepmc   +1 more source

Improving drug-drug interaction prediction via in-context learning and judging with large language models. [PDF]

Front Pharmacol
Qi H, Li X, Zhang C, Zhao T.
europepmc   +1 more source

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Fast Nonnegative Tensor Factorizations with Tensor Train Model

Lobachevskii Journal of Mathematics, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shcherbakova, E. M., Tyrtyshnikov, E. E.
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Nonnegative contraction/averaging tensor factorization

10th Symposium on Neural Network Applications in Electrical Engineering, 2010
Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potential applications, including multiway clustering, multi ...
Marko V. Jankovic, Branimir Reljin
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NONNEGATIVE TENSOR FACTORIZATION FOR CONTINUOUS EEG CLASSIFICATION

International Journal of Neural Systems, 2007
In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG ...
Lee, H, Kim, YD, Cichocki, A, Choi, S
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Spectral unmixing using nonnegative tensor factorization

Proceedings of the 45th annual southeast regional conference, 2007
Three major objectives in processing hyperspectral image data of an object (target) are data compression, spectral signature identification of constituent materials, and determination of their corresponding fractional abundances. Here we propose a novel approach to processing hyperspectral data using nonnegative tensor factorization (NTF), which ...
Qiang Zhang, Han Wang, Robert Plemmons, V. Paul Pauca   +3 more
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Nonnegative Tensor Factorization Accelerated Using GPGPU

IEEE Transactions on Parallel and Distributed Systems, 2011
This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including ...
Antikainen, Jukka, Havel, Jiri, Josth, Radovan, Herout, Adam, Zemcik, Pavel, Hauta-Kasari, Markku   +5 more
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Action Recognition in Videos Using Nonnegative Tensor Factorization

2010 20th International Conference on Pattern Recognition, 2010
Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor factorization is used to extract basis images that represent body parts.
Krausz B., Bauckhage C.
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Variable Selection for Efficient Nonnegative Tensor Factorization

2015 IEEE International Conference on Data Mining, 2015
Nonnegative Tensor Factorization (NTF) has become a popular tool for extracting informative patterns from tensor data. However, NTF has high computational cost both in space and in time, mostly in iterative calculation of the gradient. In this paper, we consider variable selection to reduce the cost, assuming sparsity of the factor matrices.
Keigo Kimura, Mineichi Kudo
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