The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
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. ...
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 ...
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
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
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
Advances in Intelligent Systems and Computing
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
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
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
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
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
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
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
Drug Discovery Today
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
IBM Systems Journal
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
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
« Previous Showing results 1 — 15 out of 1,379 results