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Page 6493 of Mathematical Reviews Vol. , Issue 89K
[page]
1989
Mathematical Reviews
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Stoer (D-WRZB-A)
89k:90169 90C35 90C30
Zenios, Stavros A. (1-PAWH-DS); Mulvey, John M. (1-PRIN-C) Vectorization and multitasking of nonlinear network programming algorithms.
Math. ...
(I-CLBR-SS)
Numerical comparisons of nonlinear programming algorithms on serial and vector processors using automatic differentiation.
Math. Programming 42 (1988), no. 2 (Ser. B), 375-389. ...
Page 5852 of Mathematical Reviews Vol. , Issue 89J
[page]
1989
Mathematical Reviews
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Summary: “The concept of multitasking mathematical programs is discussed, and an application of multitasking to the multiple-
909 ECONOMICS, OPERATIONS RESEARCH, PROGRAMMING, GAMES
5852
cost-row linear ...
Summary (translated from the Russian): “We give criteria for optimality and suboptimality in a piecewise-linear problem on a network, where the nonlinearity is a result of applying the operation of taking ...
Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast
2022
Axioms
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Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. ...
A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/axioms11060292
fatcat:owxeawqcgzafxm7od2jxogpzz4
Multitask Learning with CTC and Segmental CRF for Speech Recognition
[article]
2017
arXiv
pre-print
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labels and durations, and the latter classifies each frame as either an output symbol or a "continuation" of the previous label. ...
In this paper, we train a recognition model by optimizing an interpolation between the SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is used for feature extraction for both ...
RNN(·) denotes the nonlinear recurrence operation used in an RNN, which takes the previous hidden state and the feature vector at the current timestep as inputs, and produce an updated hidden state vector ...
arXiv:1702.06378v4
fatcat:52fnlhsfd5g4delr2guywny55e
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named Diff-DAC, with application to single-task and to average multitask reinforcement learning (MRL). ...
of a linear program. ...
Acknowledgements We thank Haitham Bou-Ammar and Peter Vrancx for insightful discussions. ...
arXiv:1710.10363v6
fatcat:efo5frxuzncsfmnpkekz2d4f4m
Artificial Intelligence in Biological Activity Prediction
[chapter]
2019
Advances in Intelligent Systems and Computing
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We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks. ...
Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. ...
Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTec-Norte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope ...
doi:10.1007/978-3-030-23873-5_20
fatcat:hrdvbvpiyzahjjnrwvedpaxfde
EchoFilter: End-to-End Neural Network for Acoustic Echo Cancellation
[article]
2021
arXiv
pre-print
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The network is trained using multitask learning by employing an auxiliary classification network for double-talk detection. ...
periods in background noise and nonlinear distortion scenarios. ...
To overcome this problem, numeric methods have been proposed, such as Nonlinear AEC (NLAEC) method by using a set of nonlinear basis functions for echo This work was supported by National Key R&D Program ...
arXiv:2105.14666v1
fatcat:vzaz347kejbk5ag63jlgqiqj5e
On Learning High Dimensional Structured Single Index Models
[article]
2016
arXiv
pre-print
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Estimation in this context requires learning both the feature weights and the nonlinear function that relates features to observations. ...
networks with significantly less computational cost. ...
Finally, neural networks have emerged as a powerful alternative to learn nonlinear transfer functions that can be basically thought of being defined by compositions of nonlinear functions. ...
arXiv:1603.03980v2
fatcat:m2y6hz2ghbgjxlvf7ng3e5ma74
Table of Contents
2022
IEEE Transactions on Cybernetics
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Li 3867 Fixed-Time Fuzzy Control for a Class of Nonlinear Systems . . . . . . . . . . . . . .Y. Sun, F. Wang, Z. Liu, Y. Zhang, and C. L. P. ...
Zhao 3971 Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tcyb.2022.3173165
fatcat:hfg4tdb5d5fjrdvrzxkstwfq2a
Intelligent Modeling and Verification 2014
2014
Journal of Applied Mathematics
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A total of 16 papers describe optimal system modeling. They focus on system modeling, optimal filtering and scheduling, image modeling, pattern recognition, and network modeling. ...
After the success of the previous special issue Intelligent Modeling and Verification, which was published last year, we are pleased to announce that the new special issue Intelligent Modeling and Verification ...
In addition, we would like to express our appreciation for the editorial board members of this journal, who provided valuable help and support throughout the preparation of this special issue. ...
doi:10.1155/2014/632027
fatcat:qfrhxkghfnccrnjm5mqxygzcjy
Data Mining and Soft Computing using Support Vector Machine: A Survey
2013
International Journal of Computer Applications
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It works better than KNN and nonlinear SVM. In future by extends this method, it will useful for variety of application domains such as climate modeling and dynamic network analysis. ...
In soft computing, neural network is a type of network includes many simple processors and each processor should have small amount of local memory. ...
doi:10.5120/13554-1367
fatcat:qpwndgz4qbclflfz3oardksh74
A survey Paper on Feature Extraction and Classification in ECG Biometric Signal
2017
IJARCCE
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Weights are allocated to the features inside the kernel of each task. We break down the network comprising of all the feature weights into sparse and low rank components. ...
In this paper, multitask learning approach is proposed in which highlight extraction and classifier configuration are completed all the while. ...
The resulting convex algorithms keep the global solution properties of support vector machines. ...
doi:10.17148/ijarcce.2017.63123
fatcat:6igbreq6jnhubpo7qsx6fcujae
Advanced machine-learning techniques in drug discovery
2020
Drug Discovery Today
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Such limitations include their need for big data, sparsity in data, and their lack of interpretability. ...
In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. ...
Acknowledgements The authors thank the Engineering and Physical Sciences Research Council (EPSRC), UK for its financial support (EP/S009000/1). ...
doi:10.1016/j.drudis.2020.12.003
pmid:33290820
fatcat:es4pvfn6xjemnluslaowd3x75u
Engineering and Scientific Subroutine Library Release 3 for IBM ES/3090 vector multiprocessors [Technical note]
1989
IBM Systems Journal
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He has worked in the areas of nonlinear differential equa- tions, linear algebra, symbolic computation, computer-aided de- sign of networks, design and analysis of algorithms, and programming applications ...
network analysis and design. ...
doi:10.1147/sj.282.0345
fatcat:e7aclq2b6zgdbmwohe223ed334
Advances in neural information processing systems 8: Proceedings of the 1995 conference
1997
Computers and Mathematics with Applications
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Constructive algorithms for hierarchical mixtures of experts (S.R. Waterhouse and A.J. Robinson). An information-theoretic learning algorithm for neural network classification (D. Miller, A. Rao, K. ...
Optimal asset allocation using adaptive dynamic programming (R. Neuneier). Using the future to "sort out" the present: Rankprop and multitask learning for medical risk evaluation (it. Caruana, S. ...
doi:10.1016/s0898-1221(97)90029-8
fatcat:6b5tr2ej4vepdktke4h2n3szfy
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