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mass of the clean pointsx which could have given rise to [UNK]. the autoencoder thus learns a vector field g ( f ( x ) ) −x indicated by the green arrows. this vector field estimates the score ∇xlog pdata ( x ) up to a multiplicative factor that is the average root mean square reconstruction error. 512 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 527 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders 14. 5. 1 estimating the score score matching (, ) is an alternative to maximum likelihood. it hyvarinen 2005 provides a consistent estimator of probability distributions based on encouraging the model to have the same score as the data distribution at every training point x. in this context, th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 528 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##oising training of a specific kind of autoencoder ( sigmoidal hidden units, linear reconstruction units ) using gaussian noise and mean squared error as the reconstruction cost is equivalent (, ) to training a specific kind vincent 2011 of undirected probabilistic model called an rbm with gaussian visible units. this k... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 528 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
infinity, then consistency is recovered. denoising score matching is discussed in more detail in section. 18. 5 other connections between autoencoders and rbms exist. score matching applied to rbms yields a cost function that is identical to reconstruction error combined with a regularization term similar to the contrac... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 528 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders by training with the squared error criterion | | g f ( ( [UNK] x ) ) − | | 2 ( 14. 16 ) and corruption c ( [UNK] = [UNK] x | ) = ( n [UNK] x ; = µ, σ σ = 2 i ) ( 14. 17 ) with noise variance σ2. see figure for an illustration of how this works. 14. 5 figure 14. 5 : vector field learned by a denoi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 529 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. local minima appear near the middle of the gap between two arms. when the norm of reconstruction error ( shown by the length of the arrows ) is large, it means that probability can be significantly increased by moving in the direction of the arrow, and that is mostly the case in places of low probability. the autoenco... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 529 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders why the early results (, ) are specialized to particular parametrizations vincent 2011 where g ( f ( x ) ) −x may be obtained by taking the derivative of another function. kamyshanska and memisevic 2015 vincent 2011 ( ) generalized the results of ( ) by identifying a family of shallow autoencod... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 530 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
1987 behnke 2001 images. denoising autoencoders are, in some sense, just mlps trained to denoise. however, the name “ denoising autoencoder ” refers to a model that is intended not merely to learn to denoise its input but to learn a good internal representation as a side [UNK] of learning to denoise. this idea came muc... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 530 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
minimizes reconstruction error in addition to a supervised objective while injecting noise in the hidden layer of a supervised mlp, with the objective to improve generalization by introducing the reconstruction error and the injected noise. however, their method was based on a linear encoder and could not learn functio... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 530 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders autoencoders take this idea further and aim to learn the structure of the manifold. to understand how autoencoders do this, we must present some important characteristics of manifolds. an important characterization of a manifold is the set of its tangent planes. at a point x on a d - dimensiona... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 531 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ization penalty. this can be an architec - tural constraint that limits the capacity of the autoencoder, or it can be a regularization term added to the reconstruction cost. these techniques generally prefer solutions that are less sensitive to the input. clearly, neither force alone would be useful — copying the inp... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 531 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
only sensitive to changes along the manifold directions, but that is insensitive to changes orthogonal to the manifold. a one - dimensional example is illustrated in figure, showing that, by making 14. 7 the reconstruction function insensitive to perturbations of the input around the data points, we cause the autoencode... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 531 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders figure 14. 6 : an illustration of the concept of a tangent hyperplane. here we create a one - dimensional manifold in 784 - dimensional space. we take an mnist image with 784 pixels and transform it by translating it vertically. the amount of vertical translation defines a coordinate along a one... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 532 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders x0 x 1 x2 x 0 0. 0 2. 0 4. 0 6. 0 8. 1 0. r x ( ) identity optimal reconstruction figure 14. 7 : if the autoencoder learns a reconstruction function that is invariant to small perturbations near the data points, it captures the manifold structure of the data. here the manifold structure is a co... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 533 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
does the same for the encoder. although the derivative ofr ( x ) is asked to be small around the data points, it can be large between the data points. the space between the data points corresponds to the region between the manifolds, where the reconstruction function must have a large derivative in order to map corrupt... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 533 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the initial machine learning research on learning nonlinear manifolds has focused on non - parametric methods based on the nearest - neighbor graph. this graph has one node per training example and edges connecting near neighbors to each other. these methods ( scholkopf et al., ; 1998 roweis and saul 2000 tenenbaum 200... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 533 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders figure 14. 8 : non - parametric manifold learning procedures build a nearest neighbor graph in which nodes represent training examples a directed edges indicate nearest neighbor relationships. various procedures can thus obtain the tangent plane associated with a neighborhood of the graph as we... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 534 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
its neighbors, as illustrated in figure. 14. 8 a global coordinate system can then be obtained through an optimization or solving a linear system. figure illustrates how a manifold can be tiled by a 14. 9 large number of locally linear gaussian - like patches ( or “ pancakes, ” because the gaussians are flat in the tange... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 534 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders figure 14. 9 : if the tangent planes ( see figure ) at each location are known, then they 14. 6 can be tiled to form a global coordinate system or a density function. each local patch can be thought of as a local euclidean coordinate system or as a locally flat gaussian, or “ pancake, ” with a ve... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 535 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
that can be [UNK] to capture from only local interpolation. consider for example the manifold resulting from translation shown in figure. if we watch just one coordinate within the input vector, 14. 6 xi, as the image is translated, we will observe that one coordinate encounters a peak or a trough in its value once for ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 535 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders 14. 7 contractive autoencoders the contractive autoencoder (,, ) introduces an explicit regularizer rifai et al. 2011a b on the code h = f ( x ), encouraging the derivatives of f to be as small as possible : ω ( ) = h λ ∂f ( ) x ∂x 2 f. ( 14. 18 ) the penalty ω ( h ) is the squared frobenius no... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 536 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
function resist small but finite - sized perturbations of the input, while contractive autoencoders make the feature extraction function resist infinitesimal perturbations of the input. when using the jacobian - based contractive penalty to pretrain features f ( x ) for use with a classifier, the best classification accura... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 536 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
cae is contractive only locally — all perturbations of a training point x are mapped near to f ( x ). globally, two [UNK] points x and xmay be mapped to f ( x ) and f ( x ) points that are farther apart than the original points. it is plausible that f be expanding in - between or far from the data manifolds ( see for e... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 536 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders is said to be contractive if the norm of jx remains less than or equal to for 1 all unit - norm x. in other words, j is contractive if it shrinks the unit sphere. we can think of the cae as penalizing the frobenius norm of the local linear approximation of f ( x ) at every training point x in o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 537 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
hidden units, corresponding to a small number of directions in the input, may have significant derivatives. the goal of the cae is to learn the manifold structure of the data. directions x with large jx rapidly change h, so these are likely to be directions which approximate the tangent planes of the manifold. experimen... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 537 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
pose, as shown in figure. visualizations 14. 6 of the experimentally obtained singular vectors do seem to correspond to meaningful transformations of the input image, as shown in figure. 14. 10 one practical issue with the cae regularization criterion is that although it is cheap to compute in the case of a single hidden... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 537 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders input point tangent vectors local pca ( no sharing across regions ) contractive autoencoder figure 14. 10 : illustration of tangent vectors of the manifold estimated by local pca and by a contractive autoencoder. the location on the manifold is defined by the input image of a dog drawn from the ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 538 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. for example, the encoder could consist of multiplying the input by a small constant and the decoder could consist of dividing the code by. as approaches, the encoder drives the 0 contractive penalty ω ( h ) to approach without having learned anything about the 0 distribution. meanwhile, the decoder maintains perfect ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 538 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
applied to unsupervised feature learning for object recognition in images and video ( kavukcuoglu 2009 2010 jarrett 2009 farabet 2011 et al.,, ; et al., ; et al., ), as well as for audio (, ). the model consists of an encoder [UNK] al. 2011 f ( x ) and a decoder g ( h ) that are both parametric. during training, h is c... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 538 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders optimization algorithm. training proceeds by minimizing | | − | | x g ( ) h 2 + λ | | h 1 + ( ) γ f | | − h x | | 2. ( 14. 19 ) like in sparse coding, the training algorithm alternates between minimization with respect to h and minimization with respect to the model parameters. minimization wit... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 539 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. in section, this topic is developed further. the tools presented in chapter 19. 5 19 make it clear that psd can be interpreted as training a directed sparse coding probabilistic model by maximizing a lower bound on the log - likelihood of the model. in practical applications of psd, the iterative optimization is only... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 539 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##akhutdinov 2006 ( ) trained a stack of rbms and then used their weights to initialize a deep autoencoder with gradually smaller hidden layers, culminating in a bottleneck of 30 units. the resulting code yielded less reconstruction error than pca into 30 dimensions and the learned representation was qualitatively easi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 539 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 14. autoencoders many forms of dimensionality reduction place semantically related examples near each other, as observed by salakhutdinov and hinton 2007b torralba ( ) and et al. ( ). the hints provided by the mapping to the lower - dimensional space aid 2008 generalization. one task that benefits even more than... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 540 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. we can also search over slightly less similar entries very [UNK], just by flipping individual bits from the encoding of the query. this approach to information retrieval via dimensionality reduction and binarization is called semantic hashing ( salakhutdinov and hinton 2007b 2009b,, ), and has been applied to both tex... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 540 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
information as possible, the network must increase the magnitude of the inputs to the sigmoid function, until saturation occurs. the idea of learning a hashing function has been further explored in several directions, including the idea of training the representations so as to optimize a loss more directly linked to th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 540 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15 representation learning in this chapter, we first discuss what it means to learn representations and how the notion of representation can be useful to design deep architectures. we discuss how learning algorithms share statistical strength across [UNK] tasks, including using information from unsupervised task... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 541 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
divide 210 by 6 using long division. the task becomes considerably less straightforward if it is instead posed using the roman numeral representation of the numbers. most modern people asked to divide ccx by vi would begin by converting the numbers to the arabic numeral representation, permitting long division procedur... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 541 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning another? generally speaking, a good representation is one that makes a subsequent learning task easier. the choice of representation will usually depend on the choice of the subsequent learning task. we can think of feedforward networks trained by supervised learning as per - forming... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 542 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##a, ). the features in the penultimate layer should learn [UNK] properties depending on the type of the last layer. supervised training of feedforward networks does not involve explicitly imposing any condition on the learned intermediate features. other kinds of representation learning algorithms are often explicitly... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 542 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
particularly interesting because it provides one way to perform unsupervised and semi - supervised learning. we often have very large amounts of unlabeled training data and relatively little labeled training data. training with supervised learning techniques on the labeled subset often results in severe overfitting. sem... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 542 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning not yet know how this is possible. many factors could explain improved human performance — for example, the brain may use very large ensembles of classifiers or bayesian inference techniques. one popular hypothesis is that the brain is able to leverage unsupervised or semi - supervise... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 543 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
task ( unsupervised learning, trying to capture the shape of the input distribution ) can sometimes be useful for another task ( supervised learning with the same input domain ). greedy layer - wise unsupervised pretraining relies on a single - layer represen - tation learning algorithm such as an rbm, a single - layer... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 543 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
fukushima 1975, ). the deep learning renaissance of 2006 began with the discovery that this greedy learning procedure could be used to find a good initialization for a joint learning procedure over all the layers, and that this approach could be used to successfully train even fully connected architectures ( hinton 2006... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 543 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning rithm, meaning that it optimizes each piece of the solution independently, one piece at a time, rather than jointly optimizing all pieces. it is called layer - wise because these independent pieces are the layers of the network. specifically, greedy layer - wise pretraining proceeds o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 544 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and a form of parameter initialization. it is common to use the word “ pretraining ” to refer not only to the pretraining stage itself but to the entire two phase protocol that combines the pretraining phase and a supervised learning phase. the supervised learning phase may involve training a simple classifier on top of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 544 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
salakhutdinov 2006, ) and probabilistic models with many layers of latent variables. such models include deep belief networks (, ) and deep hinton et al. 2006 boltzmann machines ( salakhutdinov and hinton 2009a, ). these deep generative models will be described in chapter. 20 as discussed in section, it is also possibl... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 544 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning algorithm 15. 1 greedy layer - wise unsupervised pretraining protocol. given the following : unsupervised feature learning algorithm l, which takes a training set of examples and returns an encoder or feature function f. the raw input data is x, with one row per example and f ( 1 ) (... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 545 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##t ( x y ) end if return f 2006 bengio 2007 ranzato 2007a ; et al., ; et al., ). on many other tasks, however, unsupervised pretraining either does not confer a benefit or even causes noticeable harm. ( ) studied the [UNK] of pretraining on machine learning ma et al. 2015 models for chemical activity prediction and fou... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 545 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
it is also possible to 7. 13 train an autoencoder or generative model at the same time as the supervised model. examples of this single - stage approach include the discriminative rbm ( larochelle and bengio 2008, ) and the ladder network (, ), in which the total rasmus et al. 2015 objective is an explicit sum of the t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 545 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning the idea that the choice of initial parameters for a deep neural network can have a significant regularizing [UNK] on the model ( and, to a lesser extent, that it can improve optimization ). second, it makes use of the more general idea that learning about the input distribution can h... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 546 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
remains possible that pretraining initializes the model in a location that would otherwise be inaccessible — for example, a region that is surrounded by areas where the cost function varies so much from one example to another that minibatches give only a very noisy estimate of the gradient, or a region surrounded by ar... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 546 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ed phase to perform better in the supervised learning stage, is better understood. the basic idea is that some features that are useful for the unsupervised task may also be useful for the supervised learning task. for example, if we train a generative model of images of cars and motorcycles, it will need to know abo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 546 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning top of pretrained features, the features must make the underlying classes linearly separable. these properties often occur naturally but do not always do so. this is another reason that simultaneous supervised and unsupervised learning can be preferable — the constraints imposed by t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 547 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, perhaps because images already lie in a rich vector space where distances provide a low quality similarity metric. from the point of view of unsupervised pretraining as a regularizer, we can expect unsupervised pretraining to be most helpful when the number of labeled examples is very small. because the source of inf... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 547 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
per class ). these [UNK] were also documented in carefully controlled experiments by paine 2014 et al. ( ). other factors are likely to be involved. for example, unsupervised pretraining is likely to be most useful when the function to be learned is extremely complicated. unsupervised learning [UNK] from regularizers l... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 547 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning figure 15. 1 : visualization via nonlinear projection of the learning trajectories of [UNK] neural networks in function space ( not parameter space, to avoid the issue of many - to - one mappings from parameter vectors to functions ), with [UNK] random initializations and with or wit... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 548 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
corresponding to the region of functions that produce approximately uniform distributions over the class y for most inputs ). over time, learning moves the function outward, to points that make strong predictions. training consistently terminates in one region when using pretraining and in another, non - overlapping re... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 548 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning reducing test set error. however, unsupervised pretraining can help tasks other than classification, and can act to improve optimization rather than being merely a regularizer. for example, it can improve both train and test reconstruction error for deep autoencoders ( hinton and sala... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 549 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
such as stochasticity or poor conditioning of the hessian. neural networks that receive unsupervised pretraining consistently halt in the same region of function space, while neural networks without pretraining consistently halt in another region. see figure for a visualization of this phenomenon. the region where pretr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 549 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
modern techniques for training very deep networks ( rectified linear units, dropout and batch normalization ) so less is known about the [UNK] of unsupervised pretraining in conjunction with contemporary approaches. an important question is how unsupervised pretraining can act as a regularizer. one hypothesis is that pr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 549 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning very many hyperparameters, whose [UNK] may be measured after the fact but is often [UNK] to predict ahead of time. when we perform unsupervised and supervised learning simultaneously, instead of using the pretraining strategy, there is a single hyperparameter, usually a [UNK] attache... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 550 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and being able to update them using feedback from the second phase. the most principled approach is to use validation set error in the supervised phase in order to select the hyperparameters of the pretraining phase, as discussed in ( ). in practice, some hyperparameters, larochelle et al. 2009 like the number of pretr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 550 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
learn a good representation ( typically of words, but also of sentences ), and then use this representation or fine - tune it for a supervised task for which the training set contains substantially fewer examples. this approach was pioneered by by collobert and weston 2008b turian 2010 collobert ( ), et al. ( ), and et ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 550 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning and continues to influence contemporary approaches. the idea of pretraining has been generalized to supervised pretraining discussed in section, as a very 8. 7. 4 common approach for transfer learning. supervised pretraining for transfer learning is popular (, ; oquab et al. 2014 yosi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 551 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
representations between an unsupervised learning task and a supervised learning task. in transfer learning, the learner must perform two or more [UNK] tasks, but we assume that many of the factors that explain the variations in p1 are relevant to the variations that need to be captured for learning p2. this is typicall... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 551 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
general, transfer learning, multi - task learning ( section ), and domain 7. 7 adaptation can be achieved via representation learning when there exist features that are useful for the [UNK] settings or tasks, corresponding to underlying factors that appear in more than one setting. this is illustrated in figure, with 7.... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 551 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning illustrated in figure. 15. 2 selection switch h ( 1 ) h ( 1 ) h ( 2 ) h ( 2 ) h ( 3 ) h ( 3 ) y h ( shared ) h ( shared ) x ( 1 ) x ( 1 ) x ( 2 ) x ( 2 ) x ( 3 ) x ( 3 ) figure 15. 2 : example architecture for multi - task or transfer learning when the output variable has the same sem... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 552 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the optimal input - to - output mapping ) remains the same between each setting, but the input distribution is slightly [UNK]. for example, consider the task of sentiment analysis, which consists of determining whether a comment expresses positive or negative sentiment. comments posted on the web come from many categor... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 552 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning multi - task learning. while the phrase “ multi - task learning ” typically refers to supervised learning tasks, the more general notion of transfer learning is applicable to unsupervised learning and reinforcement learning as well. in all of these cases, the objective is to take adv... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 553 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
p1 ), illustrating examples of some set of categories. the participants must use this to learn a good feature space ( mapping the raw input to some representation ), such that when we apply this learned transformation to inputs from the transfer setting ( distribution p2 ), a linear classifier can be trained and general... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 553 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
labeled examples are given at all for the zero - shot learning task. one - shot learning ( fei - fei 2006 et al., ) is possible because the representation learns to cleanly separate the underlying classes during the first stage. during the transfer learning stage, only one labeled example is needed to infer the label of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 553 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning it may be possible to recognize a specific object class even without having seen an image of that object, if the text describes the object well enough. for example, having read that a cat has four legs and pointy ears, the learner might be able to guess that an image is a cat, without... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 554 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
about cats, the output is a binary variable y with y = 1 indicating “ yes ” and y = 0 indicating “ no. ” the task variable t then represents questions to be answered such as “ is there a cat in this image? ” if we have a training set containing unsupervised examples of objects that live in the same space as t, we may b... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 554 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
similar phenomenon happens in machine translation ( klementiev 2012 et al., ; mikolov 2013b gouws 2014 et al., ; et al., ) : we have words in one language, and the relationships between words can be learned from unilingual corpora ; on the other hand, we have translated sentences which relate words in one language with... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 554 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning hx = fx ( ) x xtest ytest hy = fy ( ) y y−space relationship between embedded points within one of the domains maps between representation spaces fx fy x−space ( ) pairs in the training set x y, fx : encoder function for x fy : encoder function for y figure 15. 3 : transfer learning ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 555 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##irectional arrows. labeled examples ( dashed horizontal lines ) are pairs ( x y, ) which allow one to learn a one - way or two - way map ( solid bidirectional arrow ) between the representationsfx ( x ) and the representations f y ( y ) and anchor these representations to each other. zero - data learning is then enab... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 555 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning in one modality, a representation in the other, and the relationship ( in general a joint distribution ) between pairs ( x y, ) consisting of one observation x in one modality and another observation y in the other modality ( srivastava and salakhutdinov, 2012 ). by learning all thre... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 556 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
causes, so that the representation disentangles the causes from one another. this hypothesis motivates approaches in which we first seek a good representation for p ( x ). such a representation may also be a good representation for computing p ( y x | ) if y is among the most salient causes of x. this idea has guided a ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 556 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##tivating semi - supervised learning via unsupervised representation learning is that for many ai tasks, these two properties coincide : once we are able to obtain the underlying explanations for what we observe, it generally becomes easy to isolate individual attributes from the others. specifically, if a representati... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 556 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning x p x ( ) y = 1 y = 2 y = 3 figure 15. 4 : example of a density over x that is a mixture over three components. the component identity is an underlying explanatory factor, y. because the mixture components ( e. g., natural object classes in image data ) are statistically salient, jus... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 557 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
| p ( ) x if y is closely associated with one of the causal factors of x, then p ( x ) and p ( y x | ) will be strongly tied, and unsupervised representation learning that tries to disentangle the underlying factors of variation is likely to be useful as a semi - supervised learning strategy. consider the assumption th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 557 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning structure, with h as a latent variable that explains the observed variations in x. the “ ideal ” representation learning discussed above should thus recover these latent factors. if y is one of these ( or closely related to one of them ), then it will be very easy to learn to predict... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 558 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ed learner does not know which hi. the brute force solution is for an unsupervised learner to learn a representation that captures the reasonably all salient generative factors hj and disentangles them from each other, thus making it easy to predict from, regardless of which h y h i is associated with. y in practice,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 558 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to use a supervised learning signal at the same time as the unsupervised learning signal so that the model will choose to capture the most relevant factors of variation, or to use much larger representations if using purely unsupervised learning. an emerging strategy for unsupervised learning is to modify the definition... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 558 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning input reconstruction figure 15. 5 : an autoencoder trained with mean squared error for a robotics task has failed to reconstruct a ping pong ball. the existence of the ping pong ball and all of its spatial coordinates are important underlying causal factors that generate the image an... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 559 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
could be considered extremely salient. one way to implement such a definition of salience is to use a recently developed approach called generative adversarial networks (, ). goodfellow et al. 2014c in this approach, a generative model is trained to fool a feedforward classifier. the feedforward classifier attempts to rec... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 559 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning ground truth mse adversarial figure 15. 6 : predictive generative networks provide an example of the importance of learning which features are salient. in this example, the predictive generative network has been trained to predict the appearance of a 3 - d model of a human head at a ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 560 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ly provided by ( ). lotter et al. 2015 recognizable shape and consistent position means that a feedforward network can easily learn to detect them, making them highly salient under the generative adversarial framework. see figure for example images. generative adversarial 15. 6 networks are only one step toward determ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 560 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. very often, when we consider changes in distribution due to [UNK] domains, temporal non - stationarity, or changes in the nature of the task, the causal mechanisms remain invariant ( the laws of the universe are constant ) while the marginal distribution over the underlying causes can change. hence, better generaliza... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 560 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning be expected via learning a generative model that attempts to recover the causal factors and. h p ( ) x h | 15. 4 distributed representation distributed representations of concepts — representations composed of many ele - ments that can be set separately from each other — are one of t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 561 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
which can take 2n configurations, each potentially corresponding to a [UNK] region in input space, as illustrated in figure. this can be compared with 15. 7 a symbolic representation, where the input is associated with a single symbol or category. if there are n symbols in the dictionary, one can imagine n feature detect... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 561 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
representations include : • clustering methods, including the k - means algorithm : each input point is assigned to exactly one cluster. • k - nearest neighbors algorithms : one or a few templates or prototype examples are associated with a given input. in the case of k > 1, there are multiple 546 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 561 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning h1 h2 h3 h = [ 1,, 1 1 ] h = [ 0,, 1 1 ] h = [ 1,, 0 1 ] h = [ 1,, 1 0 ] h = [ 0,, 1 0 ] h = [ 0,, 0 1 ] h = [ 1,, 0 0 ] figure 15. 7 : illustration of how a learning algorithm based on a distributed representation breaks up the input space into regions. in this example, there are th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 562 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the representation as a whole takes on a unique value at each possible intersection of these half - planes. for example, the representation value [ 1, 1, 1 ] corresponds to the region h + 1 ∩h + 2 ∩h + 3. compare this to the non - distributed representations in figure. in the general case of 15. 8 d input dimensions, a ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 562 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
( w w log ) where w is the number of weights (, ). the combination of a powerful representation sontag 1998 layer and a weak classifier layer can be a strong regularizer ; a classifier trying to learn the concept of “ person ” versus “ not a person ” does not need to assign a [UNK] class to an input represented as “ woma... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 562 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning values describing each input, but they can not be controlled separately from each other, so this does not qualify as a true distributed representation. • decision trees : only one leaf ( and the nodes on the path from root to leaf ) is activated when an input is given. • gaussian mix... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 563 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
words being w1 and w2, for example. separate parameters are estimated for each leaf of the tree ( with some sharing being possible ). for some of these non - distributed algorithms, the output is not constant by parts but instead interpolates between neighboring regions. the relationship between the number of parameter... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 563 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
models that operate on distributed representations of words generalize much better than other models that operate directly on one - hot representations of words, as discussed in section. distributed representations induce a rich 12. 4 similarity space, in which semantically close concepts ( or inputs ) are close in dis... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 563 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning figure 15. 8 : illustration of how the nearest neighbor algorithm breaks up the input space into [UNK] regions. the nearest neighbor algorithm provides an example of a learning algorithm based on a non - distributed representation. [UNK] non - distributed algorithms may have [UNK] ge... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 564 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 15. representation learning have a statistical advantage when an apparently complicated structure can be compactly represented using a small number of parameters. some traditional non - distributed learning algorithms generalize only due to the smoothness assumption, which states that if u v ≈, then the target ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 565 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
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