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simulated annealing, which adds noise to the parameters ( kirkpatrick 1983 et al., ). continuation methods have been extremely successful in recent years. see mobahi and fisher ( ) for an overview of recent literature, especially for ai applications. 2015 continuation methods traditionally were mostly designed with the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 342 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 8. optimization for training deep models become approximately convex when blurred. in many cases, this blurring preserves enough information about the location of a global minimum that we can find the global minimum by solving progressively less blurred versions of the problem. this approach can break down in th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 343 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
local rather than a global minimum of the original cost function. though continuation methods were mostly originally designed to deal with the problem of local minima, local minima are no longer believed to be the primary problem for neural network optimization. fortunately, continuation methods can still help. the eas... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 343 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, ). ( ) [UNK] elman 1993 sanger 1994 bengio et al. 2009 justified this strategy as a continuation method, where earlier j ( ) i are made easier by increasing the influence of simpler examples ( either by assigning their contributions to the cost function larger [UNK], or by sampling them more frequently ), and experimen... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 343 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 8. optimization for training deep models more prototypical examples and then help the learner refine the decision surface with the less obvious cases. curriculum - based strategies are more [UNK] for teaching humans than strategies based on uniform sampling of examples, and can also increase the [UNK] of other t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 344 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. in the chapters ahead, we turn to specializations of the neural network family, that allow neural networks to scale to very large sizes and process input data that has special structure. the optimization methods discussed in this chapter are often directly applicable to these specialized architectures with little or ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 344 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9 convolutional networks convolutional networks (, ), also known as lecun 1989 convolutional neural networks or cnns, are a specialized kind of neural network for processing data that has a known, grid - like topology. examples include time - series data, which can be thought of as a 1d grid taking samples at r... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 345 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##tion in a neural network. we will then describe an operation called pooling, which almost all convolutional networks employ. usually, the operation used in a convolutional neural network does not correspond precisely to the definition of convolution as used in other fields such as engineering or pure mathematics. we wi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 345 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks describes general guidelines for choosing which tools to use in which circumstances. research into convolutional network architectures proceeds so rapidly that a new best architecture for a given benchmark is announced every few weeks to months, rendering it impractical to describe the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 346 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
now suppose that our laser sensor is somewhat noisy. to obtain a less noisy estimate of the spaceship ’ s position, we would like to average together several measurements. of course, more recent measurements are more relevant, so we will want this to be a weighted average that gives more weight to recent measurements. ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 346 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
negative arguments, 0 or it will look into the future, which is presumably beyond our capabilities. these limitations are particular to our example though. in general, convolution is defined for any functions for which the above integral is defined, and may be used for other purposes besides taking weighted averages. in ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 346 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks argument ( in this example, the function w ) as the kernel. the output is sometimes referred to as the. feature map in our example, the idea of a laser sensor that can provide measurements at every instant in time is not realistic. usually, when we work with data on a computer, time wi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 347 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ensional array of parameters that are adapted by the learning algorithm. we will refer to these multidimensional arrays as tensors. because each element of the input and kernel must be explicitly stored separately, we usually assume that these functions are zero everywhere but the finite set of points for which we sto... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 347 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, j ( ) = ( ∗ ) ( ) = m n i i m, j n k m, n. ( − − ) ( ) ( 9. 5 ) usually the latter formula is more straightforward to implement in a machine learning library, because there is less variation in the range of valid values of m and. n the commutative property of convolution arises because we have flipped the kernel relat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 347 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks is useful for writing proofs, it is not usually an important property of a neural network implementation. instead, many neural network libraries implement a related function called the cross - correlation, which is the same as convolution but without flipping the kernel : s i, j i k i, ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 348 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to the kernel learned by an algorithm without the flipping. it is also rare for convolution to be used alone in machine learning ; instead convolution is used simultaneously with other functions, and the combination of these functions does not commute regardless of whether the convolution operation flips its kernel or no... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 348 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##volution usually corresponds to a very sparse matrix ( a matrix whose entries are mostly equal to zero ). this is because the kernel is usually much smaller than the input image. any neural network algorithm that works with matrix multiplication and does not depend on specific properties of the matrix structure should... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 348 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks a b c d e f g h i j k l w x y z aw + bx + ey + fz aw + bx + ey + fz bw + cx + fy + gz bw + cx + fy + gz cw + dx + gy + hz cw + dx + gy + hz ew + fx + iy + jz ew + fx + iy + jz fw + gx + jy + kz fw + gx + jy + kz gw + hx + ky + lz gw + hx + ky + lz input kernel output figure 9. 1 : an e... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 349 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks 9. 2 motivation convolution leverages three important ideas that can help improve a machine learning system : sparse interactions, parameter sharing and equivariant representations. moreover, convolution provides a means for working with inputs of variable size. we now describe each of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 350 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of the model and improves its statistical [UNK]. it also means that computing the output requires fewer operations. these improvements in [UNK] are usually quite large. if there are m inputs and n outputs, then matrix multiplication requires m n × parameters and the algorithms used in practice have o ( m n × ) runtime ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 350 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
blocks that each describe only sparse interactions. parameter sharing refers to using the same parameter for more than one function in a model. in a traditional neural net, each element of the weight matrix is used exactly once when computing the output of a layer. it is multiplied by one element of the input and then ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 350 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s2 s2 s1 s1 s3 s3 x4 x4 s4 s4 x5 x5 s5 s5 x1 x1 x2 x2 x3 x3 s2 s2 s1 s1 s3 s3 x4 x4 s4 s4 x5 x5 s5 s5 figure 9. 2 : sparse connectivity, viewed from below : we highlight one input unit, x3, and also highlight the output units in s that are [UNK] by this unit. ( top ) ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 351 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s2 s2 s1 s1 s3 s3 x4 x4 s4 s4 x5 x5 s5 s5 x1 x1 x2 x2 x3 x3 s2 s2 s1 s1 s3 s3 x4 x4 s4 s4 x5 x5 s5 s5 figure 9. 3 : sparse connectivity, viewed from above : we highlight one output unit, s3, and also highlight the input units in x that [UNK] this unit. these units are... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 352 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##1h1 h3 h3 x4 x4 h4h4 x5 x5 h5h5 g2g2 g1g1 g3 g3 g4g4 g5g5 figure 9. 4 : the receptive field of the units in the deeper layers of a convolutional network is larger than the receptive field of the units in the shallow layers. this [UNK] increases if the network includes architectural features like strided convolution ( fi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 352 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s2 s2 s1 s1 s3 s3 x4 x4 s4 s4 x5 x5 s5 s5 x1 x1 x2 x2 x3 x3 x4 x4 x5 x5 s2 s2 s1 s1 s3 s3 s4 s4 s5 s5 figure 9. 5 : parameter sharing : black arrows indicate the connections that use a particular parameter in two [UNK] models. ( top ) the black arrows indicate uses of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 353 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the runtime of forward propagation — it is still o ( k n × ) — but it does further reduce the storage requirements of the model to k parameters. recall that k is usually several orders of magnitude less than m. since m and n are usually roughly the same size, k is practically insignificant compared to m n ×. convolution... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 353 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the same way. specifically, a function f ( x ) is equivariant to a function g if f ( g ( x ) ) = g ( f ( x ) ). in the case of convolution, if we let g be any function that translates the input, i. e., shifts it, then the convolution function is equivariant to g. for example, let i be a function giving image brightness ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 353 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks mapping one image function to another image function, such that i = g ( i ) is the image function with i ( x, y ) = i ( x −1, y ). this shifts every pixel of i one unit to the right. if we apply this transformation to i, then apply convolution, the result will be the same as if we appl... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 354 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
know that some function of a small number of neighboring pixels is useful when applied to multiple input locations. for example, when processing images, it is useful to detect edges in the first layer of a convolutional network. the same edges appear more or less everywhere in the image, so it is practical to share para... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 354 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
neural networks defined by matrix multiplication with a fixed - shape matrix. convolution enables processing of some of these kinds of data. we discuss this further in section. 9. 7 9. 3 pooling a typical layer of a convolutional network consists of three stages ( see figure ). 9. 7 in the first stage, the layer performs s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 354 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks figure 9. 6 : [UNK] of edge detection. the image on the right was formed by taking each pixel in the original image and subtracting the value of its neighboring pixel on the left. this shows the strength of all of the vertically oriented edges in the input image, which can be a useful ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 355 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, making convolution roughly 60, 000 times more [UNK] computationally. of course, most of the entries of the matrix would be zero. if we stored only the nonzero entries of the matrix, then both matrix multiplication and convolution would require the same number of floating point operations to compute. the matrix would s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 355 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks convolutional layer input to layer convolution stage : a ne transform [UNK] detector stage : nonlinearity e. g., rectified linear pooling stage next layer input to layers convolution layer : a ne transform [UNK] detector layer : nonlinearity e. g., rectified linear pooling layer next lay... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 356 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks and chellappa 1988, ) operation reports the maximum output within a rectangular neighborhood. other popular pooling functions include the average of a rectangular neighborhood, the l2 norm of a rectangular neighborhood, or a weighted average based on the distance from the central pixel... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 357 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of the face. in other contexts, it is more important to preserve the location of a feature. for example, if we want to find a corner defined by two edges meeting at a specific orientation, we need to preserve the location of the edges well enough to test whether they meet. the use of pooling can be viewed as adding an infi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 357 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##gure for an example. this improves the computational [UNK] of the 9. 10 network because the next layer has roughly k times fewer inputs to process. when the number of parameters in the next layer is a function of its input size ( such as when the next layer is fully connected and based on matrix multiplication ) this... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 357 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks 0. 1 1. 0. 2 1. 1. 1. 0. 1 0. 2............ 0. 3 0. 1 1. 1. 0. 3 1. 0. 2 1............. detector stage pooling stage pooling stage detector stage figure 9. 8 : max pooling introduces invariance. ( top ) a view of the middle of the output of a convolutional layer. the bottom row shows o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 358 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks large response in pooling unit large response in pooling unit large response in detector unit 1 large response in detector unit 3 figure 9. 9 : example of learned invariances : a pooling unit that pools over multiple features that are learned with separate parameters can learn to be in... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 359 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
is leveraged by maxout networks ( goodfellow et al., 2013a ) and other convolutional networks. max pooling over spatial positions is naturally invariant to translation ; this multi - channel approach is only necessary for learning other transformations. 0. 1 1. 0. 2 1. 0. 2 0. 1 0. 1 0. 0 0. 1 figure 9. 10 : pooling wi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 359 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks some theoretical work gives guidance as to which kinds of pooling one should use in various situations (, ). it is also possible to dynamically boureau et al. 2010 pool features together, for example, by running a clustering algorithm on the locations of interesting features (, ). this... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 360 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
10. 6 some examples of complete convolutional network architectures for classification using convolution and pooling are shown in figure. 9. 11 9. 4 convolution and pooling as an infinitely strong prior recall the concept of a prior probability distribution from section. this is 5. 2 a probability distribution over the pa... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 360 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and says that these parameter values are completely forbidden, regardless of how much support the data gives to those values. we can imagine a convolutional net as being similar to a fully connected net, but with an infinitely strong prior over its weights. this infinitely strong prior says that the weights for one hidde... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 360 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks input image : 256x256x3 output of convolution + relu : 256x256x64 output of pooling with stride 4 : 64x64x64 output of convolution + relu : 64x64x64 output of pooling with stride 4 : 16x16x64 output of reshape to vector : 16, 384 units output of matrix multiply : 1, 000 units output of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 361 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
000 class probabilities input image : 256x256x3 output of convolution + relu : 256x256x64 output of pooling with stride 4 : 64x64x64 output of convolution + relu : 64x64x64 output of convolution : 16x16x1, 000 output of average pooling : 1x1x1, 000 output of softmax : 1, 000 class probabilities output of pooling with s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 361 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##tional network that processes a fixed image size. after alternating between convolution and pooling for a few layers, the tensor for the convolutional feature map is reshaped to flatten out the spatial dimensions. the rest of the network is an ordinary feedforward network classifier, as described in chapter. 6 ( center ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 361 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks says that the function the layer should learn contains only local interactions and is equivariant to translation. likewise, the use of pooling is an infinitely strong prior that each unit should be invariant to small translations. of course, implementing a convolutional net as a fully c... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 362 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
) are designed to szegedy et al. 2014a use pooling on some channels but not on other channels, in order to get both highly invariant features and features that will not underfit when the translation invariance prior is incorrect. when a task involves incorporating information from very distant locations in the input, th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 362 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##tion in the context of neural networks, we usually do not refer exactly to the standard discrete convolution operation as it is usually understood in the mathematical literature. the functions used in practice [UNK] slightly. here we describe these [UNK] in detail, and highlight some useful properties of the function... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 362 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks additionally, the input is usually not just a grid of real values. rather, it is a grid of vector - valued observations. for example, a color image has a red, green and blue intensity at each pixel. in a multilayer convolutional network, the input to the second layer is the output of t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 363 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
they are based on are not guaranteed to be commutative, even if kernel - flipping is used. these multi - channel operations are only commutative if each operation has the same number of output channels as input channels. assume we have a 4 - d kernel tensor k with element ki, j, k, l giving the connection strength betwe... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 363 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
7 ) where the summation over l, m and n is over all values for which the tensor indexing operations inside the summation is valid. in linear algebra notation, we index into arrays using a for the first entry. this necessitates the 1 −1 in the above formula. programming languages such as c and python index starting from,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 363 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks to define a separate stride for each direction of motion. see figure for an 9. 12 illustration. one essential feature of any convolutional network implementation is the ability to implicitly zero - pad the input v in order to make it wider. without this feature, the width of the represen... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 364 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
is contained entirely within the image. in matlab terminology, this is called valid convolution. in this case, all pixels in the output are a function of the same number of pixels in the input, so the behavior of an output pixel is somewhat more regular. however, the size of the output shrinks at each layer. if the inp... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 364 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the input. matlab calls this same convolution. in this case, the network can contain as many convolutional layers as the available hardware can support, since the operation of convolution does not modify the architectural possibilities available to the next layer. however, the input pixels near the border influence fewe... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 364 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s1s1 s2s2 x4 x4 x5 x5 s3s3 x1 x1 x2 x2 x3 x3 z2 z2 z1z1 z3z3 x4 x4 z4 z4 x5 x5 z5z5 s1s1 s2s2 s3s3 strided convolution downsampling convolution figure 9. 12 : convolution with a stride. in this example, we use a stride of two. ( top ) convolution with a stride length ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 365 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks........................... figure 9. 13 : the [UNK] of zero padding on network size : consider a convolutional network with a kernel of width six at every layer. in this example, we do not use any pooling, so only the convolution operation itself shrinks the network size. ( top ) in th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 366 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks in some cases, we do not actually want to use convolution, but rather locally connected layers (,, ). in this case, the adjacency matrix in the lecun 1986 1989 graph of our mlp is the same, but every connection has its own weight, specified by a 6 - d tensor w. the indices into w are re... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 367 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
oper - ation to discrete convolution with a small kernel, but without sharing parameters across locations. figure compares local connections, convolution, and full 9. 14 connections. locally connected layers are useful when we know that each feature should be a function of a small part of space, but there is no reason ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 367 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
see figure for an example. modeling interactions 9. 15 between few channels allows the network to have fewer parameters in order to reduce memory consumption and increase statistical [UNK], and also reduces the amount of computation needed to perform forward and back - propagation. it accomplishes these goals without re... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 367 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s2s2 s1s1 s3 s3 x4 x4 s4s4 x5 x5 s5s5 x1 x1 x2 x2 s1s1 s3 s3 x5 x5 s5s5 x1 x1 x2 x2 x3 x3 s2s2 s1s1 s3 s3 x4 x4 s4s4 x5 x5 s5s5 a b a b a b a b a a b c d e f g h i x4 x4 x3 x3 s4s4 s2s2 figure 9. 14 : comparison of local connections, convolution, and full connections.... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 368 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
convolutional layer with a kernel width of two pixels. this model has exactly the same connectivity as the locally connected layer. the [UNK] lies not in which units interact with each other, but in how the parameters are shared. the locally connected layer has no parameter sharing. the convolutional layer uses the sam... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 368 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks input tensor output tensor spatial coordinates channel coordinates figure 9. 15 : a convolutional network with the first two output channels connected to only the first two input channels, and the second two output channels connected to only the second two input channels. 354 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 369 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks x1 x1 x2 x2 x3 x3 s2s2 s1s1 s3 s3 x4 x4 s4s4 x5 x5 s5s5 x1 x1 x2 x2 x3 x3 s2s2 s1s1 s3 s3 x4 x4 s4s4 x5 x5 s5s5 a b a b a b a b a a b c d e f g h i x1 x1 x2 x2 x3 x3 s2s2 s1s1 s3 s3 x4 x4 s4s4 x5 x5 s5s5 a b c d a b c d a figure 9. 16 : a comparison of locally connected layers, tiled c... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 370 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of a kernel that is two pixels wide. the [UNK] between the methods lies in how they share parameters. ( top ) a locally connected layer has no sharing at all. we indicate that each connection has its own weight by labeling each connection with a unique letter. tiled convolution has a set of ( center ) t [UNK] kernels. ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 370 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks to define tiled convolution algebraically, let k be a 6 - d tensor, where two of the dimensions correspond to [UNK] locations in the output map. rather than having a separate index for each location in the output map, output locations cycle through a set of t [UNK] choices of kernel sta... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 371 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
interaction with max - pooling : the detector units of these layers are driven by [UNK] filters. if these filters learn to detect [UNK] transformed versions of the same underlying features, then the max - pooled units become invariant to the learned transformation ( see figure ). convolutional layers are hard - coded to b... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 371 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
matrix involved is a function of the convolution kernel. the matrix is sparse and each element of the kernel is copied to several elements of the matrix. this view helps us to derive some of the other operations needed to implement a convolutional network. multiplication by the transpose of the matrix defined by convolu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 371 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks implemented using a convolution in some cases, but in the general case requires a third operation to be implemented. care must be taken to coordinate this transpose operation with the forward propagation. the size of the output that the transpose operation should return depends on the ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 372 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
) for a full derivation of the equations in the goodfellow 2010 fully general multi - dimensional, multi - example case. to give a sense of how these equations work, we present the two dimensional, single example version here. suppose we want to train a convolutional network that incorporates strided convolution of ker... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 372 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
so, we can use a function g,, s ( g v ) i, j, k, l = ∂ ∂ki, j, k, l j, ( v k ) = m, n gi, m, nvj, m s k, n s l ( − × 1 ) + ( − × 1 ) +. ( 9. 11 ) if this layer is not the bottom layer of the network, we will need to compute the gradient with respect to v in order to back - propagate the error farther down. to do so, we... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 372 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks that copies its input x to an approximate reconstruction r using the function w wx. it is common for more general autoencoders to use multiplication by the transpose of the weight matrix just as pca does. to make such models convolutional, we can use the function h to perform the trans... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 373 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to [UNK] through g using c and h, but these operations are not needed for the back - propagation algorithm on any standard network architectures. generally, we do not use only a linear operation in order to transform from the inputs to the outputs in a convolutional layer. we generally also add some bias term to each o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 373 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, but also allows the model to correct for [UNK] in the image statistics at [UNK] locations. for example, when using implicit zero padding, detector units at the edge of the image receive less total input and may need larger biases. 9. 6 structured outputs convolutional networks can be used to output a high - dimension... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 373 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks [UNK] ( 1 ) [UNK] ( 1 ) [UNK] ( 2 ) [UNK] ( 2 ) [UNK] ( 3 ) [UNK] ( 3 ) h ( 1 ) h ( 1 ) h ( 2 ) h ( 2 ) h ( 3 ) h ( 3 ) x u u u v v v w w figure 9. 17 : an example of a recurrent convolutional network for pixel labeling. the input is an image tensor, with axes corresponding to image ro... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 374 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
refined as many times as we wish. the tensor of convolution kernels u is used on each step to compute the hidden representation given the input image. the kernel tensor v is used to produce an estimate of the labels given the hidden values. on all but the first step, the kernels w are convolved over [UNK] to provide inpu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 374 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
- resolution jain et al. 2007 grid of labels (,, ). finally, in principle, one could pinheiro and collobert 2014 2015 use a pooling operator with unit stride. one strategy for pixel - wise labeling of images is to produce an initial guess of the image labels, then refine this initial guess using the interactions between... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 374 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks once a prediction for each pixel is made, various methods can be used to further process these predictions in order to obtain a segmentation of the image into regions (, ; briggman et al. 2009 turaga 2010 farabet 2013 et al., ; et al., ). the general idea is to assume that large groups... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 375 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##lutional networks applied to video, see chen et al. ( ). 2010 so far we have discussed only the case where every example in the train and test data has the same spatial dimensions. one advantage to convolutional networks is that they can also process inputs with varying spatial extents. these kinds of input simply ca... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 375 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
a [UNK] size of doubly block circulant matrix for each size of input. sometimes the output of the network is allowed to have variable size as well as the input, for example if we want to assign a class label to each pixel of the input. in this case, no further design work is necessary. in other cases, the network must ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 375 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks single channel multi - channel 1 - d audio waveform : the axis we convolve over corresponds to time. we discretize time and measure the amplitude of the waveform once per time step. skeleton animation data : anima - tions of 3 - d computer - rendered characters are generated by alter -... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 376 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
convolu - tion in the time makes the model equivariant to shifts in time. us - ing convolution across the fre - quency axis makes the model equivariant to frequency, so that the same melody played in a dif - ferent octave produces the same representation but at a [UNK] height in the network ’ s output. color image data... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 376 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks note that the use of convolution for processing variable sized inputs only makes sense for inputs that have variable size because they contain varying amounts of observation of the same kind of thing — [UNK] lengths of recordings over time, [UNK] widths of observations over space, etc.... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 377 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
speed up convolution by selecting an appropriate convolution algorithm. convolution is equivalent to converting both the input and the kernel to the frequency domain using a fourier transform, performing point - wise multiplication of the two signals, and converting back to the time domain using an inverse fourier tran... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 377 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##d ) runtime and parameter storage space, while separable convolution requires o ( w d × ) runtime and parameter storage space. of course, not every convolution can be represented in this way. devising faster ways of performing convolution or approximate convolution without harming the accuracy of the model is an acti... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 377 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks 9. 9 random or unsupervised features typically, the most expensive part of convolutional network training is learning the features. the output layer is usually relatively inexpensive due to the small number of features provided as input to this layer after passing through several layer... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 378 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
small image patches, then use each learned centroid as a convolution kernel. part iii describes many more unsupervised learning approaches. learning the features with an unsupervised criterion allows them to be determined separately from the classifier layer at the top of the architecture. one can then extract the featu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 378 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##lutional network : first evaluate the performance of several convolutional network architectures by training only the last layer, then take the best of these architectures and train the entire architecture using a more expensive approach. an intermediate approach is to learn the features, but using methods that do not... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 378 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks us the opportunity to take the pretraining strategy one step further than is possible with multilayer perceptrons. instead of training an entire convolutional layer at a time, we can train a model of a small patch, as ( ) do with coates et al. 2011 k - means. we can then use the parame... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 379 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
when labeled datasets were small and computational power was more limited. today, most convolutional networks are trained in a purely supervised fashion, using full forward and back - propagation through the entire network on each training iteration. as with other approaches to unsupervised pretraining, it remains [UNK... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 379 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
begins with neuroscientific experiments long before the relevant computational models were developed. neurophysiologists david hubel and torsten wiesel collaborated for several years to determine many of the most basic facts about how the mammalian vision system works ( hubel and wiesel 1959 1962 1968,,, ). their accomp... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 379 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks their work helped to characterize many aspects of brain function that are beyond the scope of this book. from the point of view of deep learning, we can focus on a simplified, cartoon view of brain function. in this simplified view, we focus on a part of the brain called v1, also known a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 380 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
primarily just to carry the signal from the eye to v1, which is located at the back of the head. a convolutional network layer is designed to capture three properties of v1 : 1. v1 is arranged in a spatial map. it actually has a two - dimensional structure mirroring the structure of the image in the retina. for example... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 380 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
cells are invariant to small shifts in the position of the feature. this inspires the pooling units of convolutional networks. complex cells are also invariant to some changes in lighting that cannot be captured simply by pooling over spatial locations. these invariances have inspired some of the cross - channel poolin... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 380 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks nicknamed “ grandmother cells ” — the idea is that a person could have a neuron that activates when seeing an image of their grandmother, regardless of whether she appears in the left or right side of the image, whether the image is a close - up of her face or zoomed out shot of her en... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 381 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
other neurons responded to the presence of bill clinton, jennifer aniston, etc. these medial temporal lobe neurons are somewhat more general than modern convolutional networks, which would not automatically generalize to identifying a person or object when reading its name. the closest analog to a convolutional network... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 381 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of mostly feedforward activation, then it proves to be very similar to a convolutional network. convolutional networks can predict it firing rates, and also perform very similarly to ( time limited ) humans on object recognition tasks (, ). dicarlo 2013 that being said, there are many [UNK] between convolutional network... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 381 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks several eye movements called saccades to glimpse the most visually salient or task - relevant parts of a scene. incorporating similar attention mechanisms into deep learning models is an active research direction. in the context of deep learning, attention mechanisms have been most suc... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 382 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
applied to some of these problems but these applications are in their infancy. • even simple brain areas like v1 are heavily impacted by feedback from higher levels. feedback has been explored extensively in neural network models but has not yet been shown to [UNK] a compelling improvement. • while feedforward it firing... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 382 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
complex. ” it is also worth mentioning that neuroscience has told us relatively little about how to train convolutional networks. model structures with parameter sharing across multiple spatial locations date back to early connectionist models of vision (, ), but these models did not use the modern marr and poggio 1976... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 382 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 9. convolutional networks lang and hinton 1988 ( ) introduced the use of back - propagation to train time - delay neural networks ( tdnns ). to use contemporary terminology, tdnns are one - dimensional convolutional networks applied to time series. back - propagation applied to these models was not inspired by ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 383 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
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