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. d. lawrence, d. d. lee, m. sugiyama, and r. garnett, editors, advances in neural information processing systems 28, pages 1108 – 1116. curran associates, inc. 466 vinyals, o., kaiser, l., koo, t., petrov, s., sutskever, i., and hinton, g. ( 2014a ). grammar as a foreign language. technical report, arxiv : 1412. 7449. 410 vinyals, o., toshev, a., bengio, s., and erhan, d. ( 2014b ). show and tell : a neural image caption generator. arxiv 1411. 4555. 410 vinyals, o., fortunato, m., and jaitly, n. ( 2015a ). pointer networks. arxiv preprint arxiv : 1506. 03134. 418 vinyals, o., toshev, a., bengio, s., and erhan, d. ( 2015b ). show and tell : a neural image caption generator. in. arxiv
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( 3 ), 79 – 86. computer 29 451 weaver, l. and tao, n. ( 2001 ). the optimal reward baseline for gradient - based reinforce - ment learning. in proc. uai ’ 2001, pages 538 – 545. 691 weinberger, k. q. and saul, l. k. ( 2004 ). unsupervised learning of image manifolds by semidefinite programming. in, pages 988 – 995., cvpr ’ 2004 164 519 weiss, y., torralba, a., and fergus, r. ( 2008 ). spectral hashing. in, pages nips 1753 – 1760. 525 welling, m., zemel, r. s., and hinton, g. e. ( 2002 ). self supervised boosting. in advances in neural information processing systems, pages 665 – 672. 703 welling, m., hinton, g. e., and osindero, s. ( 2003a ). learning sparse topographic representations with products of student - t distributions. in. nips ’ 2002 680 welling, m., zemel, r., and hinton, g. e
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and osindero, s. ( 2003a ). learning sparse topographic representations with products of student - t distributions. in. nips ’ 2002 680 welling, m., zemel, r., and hinton, g. e. ( 2003b ). self - supervised boosting. in s. becker, s. thrun, and k. obermayer, editors, advances in neural information processing systems 15 ( nips ’ 02 ), pages 665 – 672. mit press. 622 welling, m., rosen - zvi, m., and hinton, g. e. ( 2005 ). exponential family harmoniums with an application to information retrieval. in l. saul, y. weiss, and l. bottou, editors, advances in neural information processing systems 17 ( nips ’ 04 ), volume 17, cambridge, ma. mit press. 676 werbos, p. j. ( 1981 ). applications of advances in nonlinear sensitivity analysis. in proceedings of the 10th ifip conference, 31. 8 - 4. 9, nyc, pages 762 – 770. 225 weston, j., bengio, s., and usunier, n. ( 2010 ).
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. the lack of a priori distinction between learning algorithms. neural computation, ( 7 ), 1341 – 1390. 8 116 wu, r., yan, s., shan, y., dang, q., and sun, g. ( 2015 ). deep image : scaling up image recognition. arxiv : 1501. 02876. 447 wu, z. ( 1997 ). global continuation for distance geometry problems. siam journal of optimization,, 814 – 836. 7 327 xiong, h. y., barash, y., and frey, b. j. ( 2011 ). bayesian prediction of tissue - regulated splicing using rna sequence and cellular context., bioinformatics 27 ( 18 ), 2554 – 2562. 265 xu, k., ba, j. l., kiros, r., cho, k., courville, a., salakhutdinov, r., zemel, r. s., and bengio, y. ( 2015 ). show, attend and tell : neural image caption generation with visual attention. in.,, icml ’ 2015, arxiv : 1502. 03
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zemel, r. s., and bengio, y. ( 2015 ). show, attend and tell : neural image caption generation with visual attention. in.,, icml ’ 2015, arxiv : 1502. 03044 102 410 691 yildiz, i. b., jaeger, h., and kiebel, s. j. ( 2012 ). re - visiting the echo state property. neural networks,, 1 – 9. 35 405 776
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bibliography yosinski, j., clune, j., bengio, y., and lipson, h. ( 2014 ). how transferable are features in deep neural networks? in., nips ’ 2014 325 536 younes, l. ( 1998 ). on the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates. in stochastics and stochastics models, pages 177 – 228. 612 yu, d., wang, s., and deng, l. ( 2010 ). sequential labeling using deep - structured conditional random fields. ieee journal of selected topics in signal processing. 323 zaremba, w. and sutskever, i. ( 2014 ). learning to execute. arxiv 1410. 4615. 329 zaremba, w. and sutskever, i. ( 2015 ). reinforcement learning neural turing machines. arxiv : 1505. 00521. 419 zaslavsky, t. ( 1975 ). facing up to arrangements : face - count formulas for partitions of space by hyperplanes. number no. 154 in memoirs of the american mathematical society. american mathematical society. 550 zeiler, m
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##slavsky, t. ( 1975 ). facing up to arrangements : face - count formulas for partitions of space by hyperplanes. number no. 154 in memoirs of the american mathematical society. american mathematical society. 550 zeiler, m. d. and fergus, r. ( 2014 ). visualizing and understanding convolutional networks. in. eccv ’ 14 6 zeiler, m. d., ranzato, m., monga, r., mao, m., yang, k., le, q., nguyen, p., senior, a., vanhoucke, v., dean, j., and hinton, g. e. ( 2013 ). on rectified linear units for speech processing. in. icassp 2013 460 zhou, b., khosla, a., lapedriza, a., oliva, a., and torralba, a. ( 2015 ). object detectors emerge in deep scene cnns. iclr ’ 2015, arxiv : 1412. 6856. 551 zhou, j. and troyanskaya, o. g. ( 2014 ). deep supervised and convo
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). object detectors emerge in deep scene cnns. iclr ’ 2015, arxiv : 1412. 6856. 551 zhou, j. and troyanskaya, o. g. ( 2014 ). deep supervised and convolutional generative stochastic network for protein secondary structure prediction. in. icml ’ 2014 715 zhou, y. and chellappa, r. ( 1988 ). computation of optical flow using a neural network. in neural networks, 1988., ieee international conference on, pages 71 – 78. ieee. 339 zohrer, m. and pernkopf, f. ( 2014 ). general stochastic networks for classification. in nips ’ 2014. 716 777
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index 0 - 1 loss,, 102 274 absolute value rectification, 191 accuracy, 420 activation function, 169 active constraint, 94 adagrad, 305 adaline, see adaptive linear element adam,, 307 422 adaptive linear element,,, 15 23 26 adversarial example, 265 adversarial training,,, 266 268 526 [UNK], 109 ais, see annealed importance sampling almost everywhere, 70 almost sure convergence, 128 ancestral sampling,, 576 591 ann, see artificial neural network annealed importance sampling,,, 621 662 711 approximate bayesian computation, 710 approximate inference, 579 artificial intelligence, 1 artificial neural network, see neural net - work asr, see automatic speech recognition asymptotically unbiased, 123 audio,,, 101 357 455 autoencoder,,, 4 353 498 automatic speech recognition, 455 back - propagation, 201 back - propagation through time, 381 backprop, see back - propagation bag of words, 467 bagging, 252 batch normalization,, 264 422 bayes error, 116 bayes ’ rule, 69 bayesian hyperparameter optimization, 433 bayesian
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time, 381 backprop, see back - propagation bag of words, 467 bagging, 252 batch normalization,, 264 422 bayes error, 116 bayes ’ rule, 69 bayesian hyperparameter optimization, 433 bayesian network, see directed graphical model bayesian probability, 54 bayesian statistics, 134 belief network, see directed graphical model bernoulli distribution, 61 bfgs, 314 bias,, 123 227 bias parameter, 109 biased importance sampling, 589 bigram, 458 binary relation, 478 block gibbs sampling, 595 boltzmann distribution, 566 boltzmann machine,, 566 648 bptt, see back - propagation through time broadcasting, 33 burn - in, 593 cae, see contractive autoencoder calculus of variations, 178 categorical distribution, see multinoulli dis - tribution cd, see contrastive divergence centering trick ( dbm ), 667 central limit theorem, 63 chain rule ( calculus ), 203 chain rule of probability, 58 778
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index chess, 2 chord, 575 chordal graph, 575 class - based language models, 460 classical dynamical system, 372 classification, 99 clique potential, see factor ( graphical model ) cnn, see convolutional neural network collaborative filtering, 474 collider, see explaining away color images, 357 complex cell, 362 computational graph, 202 computer vision, 449 concept drift, 533 condition number, 277 conditional computation, see dynamic struc - ture conditional independence,, xiii 59 conditional probability, 58 conditional rbm, 679 connectionism,, 17 440 connectionist temporal classification, 457 consistency,, 128 509 constrained optimization,, 92 235 content - based addressing, 416 content - based recommender systems, 475 context - specific independence, 569 contextual bandits, 476 continuation methods, 324 contractive autoencoder, 516 contrast, 451 contrastive divergence,,, 289 606 666 convex optimization, 140 convolution,, 327 677 convolutional network, 16 convolutional neural network,, 250 327,, 422 456 coordinate descent,, 319 665 correlation, 60 cost function
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##6 convex optimization, 140 convolution,, 327 677 convolutional network, 16 convolutional neural network,, 250 327,, 422 456 coordinate descent,, 319 665 correlation, 60 cost function, see objective function covariance,, xiii 60 covariance matrix, 61 coverage, 421 critical temperature, 599 cross - correlation, 329 cross - entropy,, 74 131 cross - validation, 121 ctc, see connectionist temporal classifica - tion curriculum learning, 326 curse of dimensionality, 153 cyc, 2 d - separation, 568 dae, see denoising autoencoder data generating distribution,, 110 130 data generating process, 110 data parallelism, 444 dataset, 103 dataset augmentation,, 268 454 dbm, see deep boltzmann machine dcgan,,, 547 548 695 decision tree,, 144 544 decoder, 4 deep belief network,,,,,, 26 525 626 651 654 678 686, deep blue, 2 deep boltzmann machine,,,,, 23 26 525 626 647 651 657 666 678,,,, deep
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,,, 26 525 626 651 654 678 686, deep blue, 2 deep boltzmann machine,,,,, 23 26 525 626 647 651 657 666 678,,,, deep feedforward network,, 166 422 deep learning,, 2 5 denoising autoencoder,, 506 683 denoising score matching, 615 density estimation, 102 derivative,, xiii 82 design matrix, 105 detector layer, 336 determinant, xii diagonal matrix, 40 [UNK] entropy,, 73 641 dirac delta function, 64 directed graphical model,,,, 76 503 559 685 directional derivative, 84 discriminative fine - tuning, see supervised fine - tuning discriminative rbm, 680 distributed representation,,, 17 149 542 domain adaptation, 532 779
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