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chapter 11. practical methodology monitor histograms of activations and gradient : it is often useful to visualize statistics of neural network activations and gradients, collected over a large amount of training iterations ( maybe one epoch ). the pre - activation value of hidden units can tell us if the units saturat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 455 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
( which would make the parameters move too slowly ). it may be that some groups of parameters are moving at a good pace while others are stalled. when the data is sparse ( like in natural language ), some parameters may be very rarely updated, and this should be kept in mind when monitoring their evolution. finally, ma... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 455 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
parameter. 11. 6 example : multi - digit number recognition to provide an end - to - end description of how to apply our design methodology in practice, we present a brief account of the street view transcription system, from the point of view of designing the deep learning components. obviously, many other components ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 455 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology numbers prior to transcribing them. the transcription project began with a choice of performance metrics and desired values for these metrics. an important general principle is to tailor the choice of metric to the business goals for the project. because maps are only useful if they ha... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 456 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
is to rapidly establish a sensible baseline system. for vision tasks, this means a convolutional network with rectified linear units. the transcription project began with such a model. at the time, it was not common for a convolutional network to output a sequence of predictions. in order to begin with the simplest poss... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 456 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
t. initially, the definition of p ( y x | ) was ad - hoc, based on simply multiplying all of the softmax outputs together. this motivated the development of a specialized output layer and cost function that actually computed a principled log - likelihood. this approach allowed the example rejection mechanism to function... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 456 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology to underfitting or due to a problem with the training data. one of the debugging strategies we recommend is to visualize the model ’ s worst errors. in this case, that meant visualizing the incorrect training set transcriptions that the model gave the highest confidence. these proved to ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 457 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of performance came from adjusting hyperparameters. this mostly consisted of making the model larger while main - taining some restrictions on its computational cost. because train and test error remained roughly equal, it was always clear that any performance deficits were due to underfitting, as well as due to a few re... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 457 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12 applications in this chapter, we describe how to use deep learning to solve applications in com - puter vision, speech recognition, natural language processing, and other application areas of commercial interest. we begin by discussing the large scale neural network implementations required for most serious ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 458 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
that the number of neurons must be large. one of the key factors responsible for the improvement in neural network ’ s accuracy and the improvement of the complexity of tasks they can solve between the 1980s and today is the dramatic increase in the size of the networks we use. as we saw in section, network sizes have ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 458 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications requires high performance hardware and software infrastructure. 12. 1. 1 fast cpu implementations traditionally, neural networks were trained using the cpu of a single machine. today, this approach is generally considered [UNK]. we now mostly use gpu computing or the cpus of many machines netwo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 459 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
performance characteristics, so sometimes floating - point implementations can be faster too. the important principle is that careful specialization of numerical computation routines can yield a large [UNK]. other strategies, besides choosing whether to use fixed or floating point, include optimizing data structures to av... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 459 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
must perform matrix multiplication and division on many vertices in parallel to convert these 3 - d coordinates into 2 - d on - screen coordinates. the graphics card must then perform many computations at each pixel in parallel to determine the color of each pixel. in both cases, the 444 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 459 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications computations are fairly simple and do not involve much branching compared to the computational workload that a cpu usually encounters. for example, each vertex in the same rigid object will be multiplied by the same matrix ; there is no need to evaluate an if statement per - vertex to determine... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 460 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
[UNK] are large enough to fall outside the cache of a traditional desktop computer so the memory bandwidth of the system often becomes the rate limiting factor. gpus [UNK] a compelling advantage over cpus due to their high memory bandwidth. neural network training algorithms typically do not involve much branching or s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 460 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##krau et al. ( ) implemented a two - layer fully connected neural network on a gpu and 2005 reported a threefold speedup over their cpu - based baseline. shortly thereafter, chellapilla 2006 et al. ( ) demonstrated that the same technique could be used to accelerate supervised convolutional networks. the popularity of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 460 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications bandwidth, gp - gpus now [UNK] an ideal platform for neural network programming. this platform was rapidly adopted by deep learning researchers soon after it became available (, ;, ). raina et al. 2009 ciresan et al. 2010 writing [UNK] code for gp - gpus remains a [UNK] task best left to spe - ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 461 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
value that they need simultaneously, as part of a single memory transaction. [UNK] models of gpus are able to coalesce [UNK] kinds of read or write patterns. typically, memory operations are easier to coalesce if among n threads, thread i accesses byte i + j of memory, and j is a multiple of some power of 2. the exact ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 461 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. typically, one can do this by building a software library of high performance operations like convolution and matrix multiplication, then specifying models in terms of calls to this library of operations. for example, the machine learning library pylearn2 ( goodfellow 2013c et al., ) specifies all of its machine learn... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 461 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications 12. 1. 3 large - scale distributed implementations in many cases, the computational resources available on a single machine are [UNK]. we therefore want to distribute the workload of training and inference across many machines. distributing inference is simple, because each input example we wan... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 462 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
t −1 this can be solved using asynchronous stochastic gradient descent ( ben - gio 2001 recht 2011 et al., ; et al., ). in this approach, several processor cores share the memory representing the parameters. each core reads parameters without a lock, then computes a gradient, then increments the parameters without a lo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 462 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
deep learning researchers typically cannot [UNK] the same scale of distributed learning systems but some research has focused on how to build distributed networks with relatively low - cost hardware available in the university setting (, ). coates et al. 2013 12. 1. 4 model compression in many commercial applications, ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 462 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications personalization, it is possible to train a model once, then deploy it to be used by billions of users. in many cases, the end user is more resource - constrained than the developer. for example, one might train a speech recognition network with a powerful computer cluster, then deploy it on mob... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 463 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
x ), but do so using many more parameters than are necessary for the task. their size is necessary only due to the limited number of training examples. as soon as we have fit this function f ( x ), we can generate a training set containing infinitely many examples, simply by applying f to randomly sampled points x. we th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 463 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
5 dynamic structure one strategy for accelerating data processing systems in general is to build systems that have dynamic structure in the graph describing the computation needed to process an input. data processing systems can dynamically determine which subset of many neural networks should be run on a given input. ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 463 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications system can run faster by computing these features only when they are needed. dynamic structure of computations is a basic computer science principle applied generally throughout the software engineering discipline. the simplest versions of dynamic structure applied to neural networks are based ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 464 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
have high recall. in other words, they are trained to make sure we do not wrongly reject an input when the object is present. the final classifier is trained to have high precision. at test time, we run inference by running the classifiers in a sequence, abandoning any example as soon as any one element in the cascade rej... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 464 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
a cascade of boosted decision trees to implement a fast and robust face detector suitable for use in handheld digital cameras. their classifier localizes a face using essentially a sliding window approach in which many windows are examined and rejected if they do not contain faces. another version of cascades uses the e... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 464 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications is to train a decision tree in which each node uses a neural network to make the splitting decision (, ), though this has typically not been guo and gelfand 1992 done with the primary goal of accelerating inference computations. in the same spirit, one can use a neural network, called the gater... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 465 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, which collobert et al. 2001 2002 can considerably accelerate training and inference time. this strategy works well when the number of gating decisions is small because it is not combinatorial. but when we want to select [UNK] subsets of units or parameters, it is not possible to use a “ soft switch ” because it requi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 465 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
units depending on the context. this dynamic routing approach can be interpreted as an attention mechanism (, ). olshausen et al. 1993 so far, the use of a hard switch has not proven [UNK] on large - scale applications. contemporary approaches instead use a weighted average over many possible inputs, and thus do not ac... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 465 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications coherence and gpu implementations will be slow due to the lack of coalesced memory transactions and the need to serialize warps when members of a warp take [UNK] branches. in some cases, these issues can be mitigated by partitioning the examples into groups that all take the same branch, and pr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 466 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
early days of neural networks research, hardware designers have worked on specialized hardware implementations that could speed up training and / or inference of neural network algorithms. see early and more recent reviews of specialized hardware for deep networks (, ;, lindsey and lindblad 1994 beiu et al. 2003 misra ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 466 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
field programmable gated array ) implementations ( where the particulars of the circuit can be written on the chip after it has been built ) have been developed. though software implementations on general - purpose processing units ( cpus and gpus ) typically use 32 or 64 bits of precision to represent floating point num... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 466 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications computing speed have come from parallelization across cores ( either in cpus or gpus ). this is very [UNK] from the situation of the 1990s ( the previous neural network era ) where the hardware implementations of neural networks ( which might take two years from inception to availability of a c... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 467 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
that more precision is required during training than at inference time, and that some forms of dynamic fixed point representation of numbers can be used to reduce how many bits are required per number. traditional fixed point numbers are restricted to a fixed range ( which corresponds to a given exponent in a floating poin... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 467 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
deep learning algorithms are forms of object recognition or optical character recognition. computer vision is a very broad field encompassing a wide variety of ways of processing images, and an amazing diversity of applications. applications of computer vision range from reproducing human visual abilities, such as recog... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 467 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications exotic applications that expand the realm of what is possible with imagery but rather a small core of ai goals aimed at replicating human abilities. most deep learning for computer vision is used for object recognition or detection of some form, whether this means reporting which object is pres... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 468 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
learning architectures to represent. computer vision usually requires relatively little of this kind of pre - processing. the images should be standardized so that their pixels all lie in the same, reasonable range, like [ 0, 1 ] or [ - 1, 1 ]. mixing images that lie in [ 0, 1 ] with images that lie in [ 0, 255 ] will ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 468 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
label each pixel in an image (, ). hadsell et al. 2007 dataset augmentation may be seen as a way of preprocessing the training set only. dataset augmentation is an excellent way to reduce the generalization error of most computer vision models. a related idea applicable at test time is to show the model many [UNK] vers... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 468 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications variation in the data can both reduce generalization error and reduce the size of the model needed to fit the training set. simpler tasks may be solved by smaller models, and simpler solutions are more likely to generalize well. preprocessing of this kind is usually designed to remove some kind ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 469 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
is the amount of contrast in the image. contrast simply refers to the magnitude of the [UNK] between the bright and the dark pixels in an image. there are many ways of quantifying the contrast of an image. in the context of deep learning, contrast usually refers to the standard deviation of the pixels in an image or re... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 469 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##n ) aims to prevent images from having varying amounts of contrast by subtracting the mean from each image, then rescaling it so that the standard deviation across its pixels is equal to some constant s. this approach is complicated by the fact that no scaling factor can change the contrast of a zero - contrast image... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 469 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications more than amplifying sensor noise or compression artifacts in such cases. this motivates introducing a small, positive regularization parameter λ to bias the estimate of the standard deviation. alternately, one can constrain the denominator to be at least. given an input image x, gcn produces a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 470 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
2013a dataset. small images cropped randomly are more likely to have nearly constant intensity, making aggressive regularization more useful. ( ) used coates et al. 2011 λ = 0 and = 10 on small, randomly selected patches drawn from cifar - 10. the scale parameter s can usually be set to, as done by ( ), 1 coates et al.... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 470 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to the standard deviation can help build a useful intuition. one can understand gcn as mapping examples to a spherical shell. see figure for an illustration. this can be a 12. 1 useful property because neural networks are often better at responding to directions in space rather than exact locations. responding to multip... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 470 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications −1 5 0 0 1 5... x0 −1 5. 0 0. 1 5. x1 raw input −1 5 0 0 1 5... x0 gcn, = 10 λ −2 −1 5 0 0 1 5... x0 gcn, = 0 λ figure 12. 1 : gcn maps examples onto a sphere. ( left ) raw input data may have any norm. ( center ) gcn with λ = 0 maps all non - zero examples perfectly onto a sphere. here we use ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 471 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
normal distribution used by pca has spherical contours. sphering is more commonly known as. whitening global contrast normalization will often fail to highlight image features we would like to stand out, such as edges and corners. if we have a scene with a large dark area and a large bright area ( such as a city square... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 471 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and dividing by a standard deviation of nearby pixels. in some cases, this is literally the mean and standard deviation of all pixels in a rectangular window centered on the pixel to be modified (, ). in other cases, this is a weighted mean pinto et al. 2008 and weighted standard deviation using gaussian weights centere... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 471 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications input image gcn lcn figure 12. 2 : a comparison of global and local contrast normalization. visually, the [UNK] of global contrast normalization are subtle. it places all images on roughly the same scale, which reduces the burden on the learning algorithm to handle multiple scales. local contra... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 472 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contrast normalization is a [UNK] operation and can also be used as a nonlinearity applied to the hidden layers of a network, as well as a preprocessing operation applied to the input. as with global contrast normalization, we typically need to regularize local contrast normalization to avoid division by zero. in fact,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 472 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications 12. 2. 1. 2 dataset augmentation as described in section, it is easy to improve the generalization of a classifier 7. 4 by increasing the size of the training set by adding extra copies of the training examples that have been modified with transformations that do not change the class. object reco... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 473 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
). lecun et al. 1998b 12. 3 speech recognition the task of speech recognition is to map an acoustic signal containing a spoken natural language utterance into the corresponding sequence of words intended by the speaker. let x = ( x ( 1 ), x ( 2 ),..., x ( ) t ) denote the sequence of acoustic input vectors ( traditiona... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 473 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
y x | x ( 12. 4 ) where p ∗is the true conditional distribution relating the inputs x to the targets y. since the 1980s and until about 2009 – 2012, state - of - the art speech recognition systems primarily combined hidden markov models ( hmms ) and gaussian mixture models ( gmms ). gmms modeled the association between... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 473 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications phoneme ), then a gmm transforms each discrete symbol into a brief segment of audio waveform. although gmm - hmm systems dominated asr until recently, speech recognition was actually one of the first areas where neural networks were applied, and numerous asr systems from the late 1980s and early... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 474 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##me recognition, playing a role similar to the role mnist plays for object recognition. however, because of the complex engineering involved in software systems for speech recognition and the [UNK] that had been invested in building these systems on the basis of gmm - hmms, the industry did not see a compelling argume... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 474 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the input data. rbms will be described in part. iii to solve speech recognition tasks, unsupervised pretraining was used to build deep feedforward networks whose layers were each initialized by training an rbm. these networks take spectral acoustic representations in a fixed - size input window ( around a center frame )... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 474 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications shifted from being based on pretraining and boltzmann machines to being based on techniques such as rectified linear units and dropout (, ; zeiler et al. 2013 dahl et al., ). by that time, several of the major speech groups in industry had 2013 started exploring deep learning in collaboration wi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 475 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
improve much with the traditional gmm - hmm technology, in spite of the continuously growing size of training sets ( see figure 2. 4 of deng and yu 2014 ( ) ). this created a rapid shift in the speech recognition community towards deep learning. in a matter of roughly two years, most of the industrial products for speec... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 475 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
recognition systems that completely remove the hmm. the first major breakthrough in this direction came from graves 2013 et al. ( ) who trained a deep lstm rnn ( see section ), using map inference over the frame - to - 10. 10 phoneme alignment, as in ( ) and in the ctc framework ( lecun et al. 1998b graves et al., ; 200... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 475 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications information (, ;, ). chorowski et al. 2014 lu et al. 2015 12. 4 natural language processing natural language processing ( nlp ) is the use of human languages, such as english or french, by a computer. computer programs typically read and emit specialized languages designed to allow [UNK] and un... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 476 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
model of natural language, we must usually use techniques that are specialized for processing sequential data. in many cases, we choose to regard natural language as a sequence of words, rather than a sequence of individual characters or bytes. because the total number of possible words is so large, word - based langua... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 476 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
token given the preceding n −1 tokens. the model uses products of these conditional distributions to define the probability distribution over longer sequences : p x ( 1,..., xτ ) = ( p x1,..., xn−1 ) τ t n = p x ( t | xt n − + 1,..., xt−1 ). ( 12. 5 ) 461 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 476 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications this decomposition is justified by the chain rule of probability. the probability distribution over the initial sequencep ( x1,..., xn−1 ) may be modeled by a [UNK] model with a smaller value of. n training n - gram models is straightforward because the maximum likelihood estimate can be compute... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 477 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
compute p x ( t | xt n − + 1,..., xt−1 ) = pn ( xt n − + 1,..., xt ) pn−1 ( xt n − + 1,..., xt−1 ) ( 12. 6 ) simply by looking up two stored probabilities. for this to exactly reproduce inference in pn, we must omit the final character from each sequence when we train pn−1. as an example, we demonstrate how a trigram mo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 477 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
tional distribution p ( away dog ran | ). putting this together with equation, 12. 6 we obtain : p p ( ) = the dog ran away 3 ( ) the dog ran p3 ( ) dog ran away / p 2 ( ) dog ran. ( 12. 7 ) a fundamental limitation of maximum likelihood for n - gram models is that pn as estimated from training set counts is very likel... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 477 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications shift probability mass from the observed tuples to unobserved ones that are similar. see ( ) for a review and empirical comparisons. one basic chen and goodman 1999 technique consists of adding non - zero probability mass to all of the possible next symbol values. this method can be justified as... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 478 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
reliable estimate is found. classical n - gram models are particularly vulnerable to the curse of dimension - ality. there are | | v n possible n - grams and | | v is often very large. even with a massive training set and modest n, most n - grams will not occur in the training set. one way to view a classical n - gram ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 478 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
share knowledge between one word and other semantically similar words. to improve the statistical [UNK] of n - gram models, class - based language models ( brown 1992 ney and kneser 1993 niesler 1998 et al., ;, ; et al., ) introduce the notion of word categories and then share statistical strength between words that ar... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 478 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications 12. 4. 2 neural language models neural language models or nlms are a class of language model designed to overcome the curse of dimensionality problem for modeling natural language sequences by using a distributed representation of words (, ). bengio et al. 2001 unlike class - based n - gram mod... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 479 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
transferring information from each training sentence to an exponentially large number of semantically related sentences. the curse of dimensionality requires the model to generalize to a number of sentences that is exponential in the sentence length. the model counters this curse by relating each training sentence to a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 479 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
show 12. 3 how semantically similar words map to representations that are close to each other. neural networks in other domains also define embeddings. for example, a hidden layer of a convolutional network provides an “ image embedding. ” usually nlp practitioners are much more interested in this idea of embeddings bec... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 479 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications multiple latent variables ( mnih and hinton 2007, ). − − − − − 34 32 30 28 26 −14 −13 −12 −11 −10 −9 −8 −7 −6 canada europe ontario north english canadian union african africa british france russian china germany french assembly eu japan iraq south european 35 0 35 5 36 0 36 5 37 0 37 5 38 0...... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 480 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##beddings typically have higher dimensionality and can simultaneously capture many kinds of similarity between words. 12. 4. 3 high - dimensional outputs in many natural language applications, we often want our models to produce words ( rather than characters ) as the fundamental unit of the output. for large vocabula... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 480 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to perform the full matrix multiplication at training time as well as test time — we cannot calculate only the dot product with the weight vector for the correct output. the high computational costs of the output layer thus arise both at training time ( to compute the likelihood and its gradient ) and at test time ( to... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 480 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications loss functions, the gradient can be computed [UNK] (, ), but vincent et al. 2015 the standard cross - entropy loss applied to a traditional softmax output layer poses many [UNK]. suppose that h is the top hidden layer used to predict the output probabilities [UNK]. if we parametrize the transfo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 481 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
) dealt with the high cost bengio et al. 2001 2003 of using a softmax over a large number of output words by limiting the vocabulary size to 10, 000 or 20, 000 words. schwenk and gauvain 2002 schwenk 2007 ( ) and ( ) built upon this approach by splitting the vocabulary v into a shortlist l of most frequent words ( hand... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 481 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
p i c ( = | ∈ − l ) ( 1 ( ∈ | t ) ) + 1i∈tp y i c, i p i c ( = | ∈t ) ( ∈ | t ) ( 12. 10 ) where p ( y = i c, i | ∈l ) is provided by the neural language model and p ( y = i | c, i ∈t ) is provided by the n - gram model. with slight modification, this approach can also work using an extra output value in the neural lang... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 481 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications words, where, arguably, it is the least useful. this disadvantage has stimulated the exploration of alternative methods to deal with high - dimensional outputs, described below. 12. 4. 3. 2 hierarchical softmax a classical approach (, ) to reducing the computational burden goodman 2001 of high ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 482 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
depth o ( log | | v ). the probability of a choosing a word is given by the product of the probabilities of choosing the branch leading to that word at every node on a path from the root of the tree to the leaf containing the word. figure illustrates a simple example. ( ) also describe 12. 4 mnih and hinton 2009 how to... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 482 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of decisions. because the output log - likelihood can be computed [UNK] ( as low as log | | v rather than | | v ), its gradients may also be computed [UNK]. this includes not only the gradient with respect to the output parameters but also the gradients with respect to the hidden layer activations. it is possible but u... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 482 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications ( 1 ) ( 0 ) ( 0, 0, 0 ) ( 0, 0, 1 ) ( 0, 1, 0 ) ( 0, 1, 1 ) ( 1, 0, 0 ) ( 1, 0, 1 ) ( 1, 1, 0 ) ( 1, 1, 1 ) ( 1, 1 ) ( 1, 0 ) ( 0, 1 ) ( 0, 0 ) w0 w0 w1 w1 w2 w2 w3 w3 w4 w4 w5 w5 w6 w6 w7 w7 figure 12. 4 : illustration of a simple hierarchy of word categories, with 8 wordsw0,..., w7 organized ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 483 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the classes ( 0, 0 ) ( 0 and, 1 ), which respectively contain the sets of words { w 0, w1 } and { w2, w3 }, and similarly super - class contains the classes ( 1 ) ( 1, 0 ) ( 1 and, 1 ), which respectively contain the words ( w4, w5 ) ( and w6, w7 ). if the tree is [UNK] balanced, the maximum depth ( number of binary de... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 483 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
be the i - th binary decision when traversing the tree towards the value y. the probability of sampling an output y decomposes into a product of conditional probabilities, using the chain rule for conditional probabilities, with each node indexed by the prefix of these bits. for example, node ( 1, 0 ) corresponds to the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 483 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications practice, the computational savings are typically not worth the [UNK] because the computation of the output probabilities is only one part of the total computation in the neural language model. for example, suppose there are l fully connected hidden layers of width nh. let nb be the weighted av... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 484 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
factor of, one can instead define a tree with depth two 2 and a branching factor of | | v. such a tree corresponds to simply defining a set of mutually exclusive word classes. the simple approach based on a tree of depth two captures most of the computational benefit of the hierarchical strategy. one question that remains... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 484 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##fits both at training time and at test time, if at test time we want to compute the probability of specific words. of course, computing the probability of all | | v words will remain expensive even with the hierarchical softmax. another important operation is selecting the most likely word in a given context. unfortuna... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 484 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications in the next position. every incorrect word should have low probability under the model. it can be computationally costly to enumerate all of these words. instead, it is possible to sample only a subset of the words. using the notation introduced in equation, the gradient can be written as follo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 485 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
p ( i c | ). since the negative phase term is an expectation, we can estimate it with a monte carlo sample. however, that would require sampling from the model itself. sampling from the model requires computing p ( i c | ) for all i in the vocabulary, which is precisely what we are trying to avoid. instead of sampling ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 485 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
are normalized to sum to 1. when negative word ni is sampled, the associated gradient is weighted by wi = pni / qn i n j = 1 pnj / qnj. ( 12. 17 ) these weights are used to give the appropriate importance to the m negative samples from q used to form the estimated negative phase contribution to the 470 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 485 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications gradient : | | v i = 1 p i c ( | ) ∂ai ∂θ ≈1 m m i = 1 wi ∂ani ∂θ. ( 12. 18 ) a unigram or a bigram distribution works well as the proposal distribution q. it is easy to estimate the parameters of such a distribution from data. after estimating the parameters, it is also possible to sample from... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 486 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
early in learning, the model may not actually choose to make the output truly sparse. moreover, the loss function we use for training might most naturally be described in terms of comparing every element of the output to every element of the target. this means that it is not always clear that there is a computational b... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 486 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
all of these cases, the computational complexity of gradient estimation for the output layer is reduced to be proportional to the number of negative samples rather than proportional to the size of the output vector. 12. 4. 3. 4 noise - contrastive estimation and ranking loss other approaches based on sampling have been... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 486 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications scores ai. the ranking loss proposed then is l = i max ( 0 1, −ay + ai ). ( 12. 19 ) the gradient is zero for the i - th term if the score of the observed word, ay, is greater than the score of the negative word ai by a margin of 1. one issue with this criterion is that it does not provide esti... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 487 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
frequencies of very many tuples ) while requiring very little computation to process an example ( by looking up only a few tuples that match the current context ). if we use hash tables or trees to access the counts, the computation used for n - grams is almost independent of capacity. in comparison, doubling a neural ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 487 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
et al.,, ). as with any ensemble, this technique can reduce test error if 2001 2003 the ensemble members make independent mistakes. the field of ensemble learning provides many ways of combining the ensemble members ’ predictions, including uniform weighting and weights chosen on a validation set. mikolov 2011a et al. (... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 487 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications a neural network with an extra set of inputs that are connected directly to the output, and not connected to any other part of the model. the extra inputs are indicators for the presence of particular n - grams in the input context, so these variables are very high - dimensional and very sparse... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 488 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the proposal mechanism suggests many variants of the suggested translation, ideally including “ red apple. ” a second component of the translation system, a language model, evaluates the proposed translations, and can score “ red apple ” as better than “ apple red. ” the earliest use of neural networks for machine tran... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 488 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
translation involves producing an output sentence given an input sentence, it makes sense to extend the natural language model to be conditional. as described in section, it is straightforward to extend a model 6. 2. 1. 1 that defines a marginal distribution over some variable to define a conditional distribution over th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 488 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 12. applications decoder output object ( english sentence ) intermediate, semantic representation source object ( french sentence or image ) encoder figure 12. 5 : the encoder - decoder architecture to map back and forth between a surface representation ( such as a sequence of words or an image ) and a semantic... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 489 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
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