標題: Titlebook: Neural Networks: Tricks of the Trade; Grégoire Montavon,Geneviève B. Orr,Klaus-Robert Mü Book 2012Latest edition Springer-Verlag Berlin He [打印本頁] 作者: FAD 時間: 2025-3-21 18:03
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作者: epicondylitis 時間: 2025-3-21 23:34
Speeding Learning since the time BP was first introduced, BP is still the most widely used learning algorithm.The reason for this is its simplicity, efficiency, and its general effectiveness on a wide range of problems. Even so, there are many pitfalls in applying it, which is where all these tricks enter.作者: gain631 時間: 2025-3-22 04:18
Early Stopping — But When? 12 problems and 24 different network architectures I conclude slower stopping criteria allow for small improvements in generalization (here: about 4% on average), but cost much more training time (here: about factor 4 longer on average).作者: VEST 時間: 2025-3-22 05:06
A Simple Trick for Estimating the Weight Decay Parametermator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute..The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stopping.作者: breadth 時間: 2025-3-22 10:50
Centering Neural Network Gradient Factorsated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network’s generalization ability.作者: Campaign 時間: 2025-3-22 16:00 作者: Commemorate 時間: 2025-3-22 19:00 作者: micronutrients 時間: 2025-3-22 22:47 作者: 勉勵 時間: 2025-3-23 01:28
Efficient BackPropations of why they work..Many authors have suggested that second-order optimization methods are advantageous for neural net training. It is shown that most “classical” second-order methods are impractical for large neural networks. A few methods are proposed that do not have these limitations.作者: Noisome 時間: 2025-3-23 09:34
Large Ensemble Averaginghoices of synaptic weights. We find that the optimal stopping criterion for large ensembles occurs later in training time than for single networks. We test our method on the suspots data set and obtain excellent results.作者: 爭吵 時間: 2025-3-23 11:50 作者: 束以馬具 時間: 2025-3-23 15:01
Avoiding Roundoff Error in Backpropagating Derivatives they are small but non-zero. This roundoff error is easily avoided with a simple programming trick which has a small memory overhead (one or two extra floating point numbers per unit) and an insignificant computational overhead.作者: 隼鷹 時間: 2025-3-23 19:18 作者: Solace 時間: 2025-3-24 02:12 作者: Fierce 時間: 2025-3-24 05:16
Square Unit Augmented, Radially Extended, Multilayer Perceptronsture has the localized properties of an RBFN but does not suffer as badly from the curse of dimensionality. I refer to a network of this type as a SQuare Unit Augmented, Radially Extended, MultiLayer Perceptron (SQUARE-MLP or SMLP).作者: 重畫只能放棄 時間: 2025-3-24 09:52 作者: VEIL 時間: 2025-3-24 14:16
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagationof tangent vectors, which compactly represent the essence of these transformation invariances, and two classes of algorithms, “tangent distance” and “tangent propagation”, which make use of these invariances to improve performance.作者: Hemiplegia 時間: 2025-3-24 17:31 作者: 滑動 時間: 2025-3-24 19:41
Introductionly apply neural networks to difficult real world problems. Often these “tricks” are theoretically well motivated. Sometimes they are the result of trial and error. However, their most common link is that they are usually hidden in people’s heads or in the back pages of space-constrained conference p作者: bioavailability 時間: 2025-3-25 00:24
Speeding Learningomplexity of our algorithms and the size of our problems will always expand to consume all cycles available, regardless of the speed of ourmachines.Thus, there will never come a time when computational efficiency can or should be ignored. Besides, in the quest to find solutions faster, we also often作者: FISC 時間: 2025-3-25 07:00
Efficient BackPropackprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers explanations of why they work..Many authors have suggested that second-order optimization methods are advantageous for neural net training. It is shown that作者: 無底 時間: 2025-3-25 07:59
Early Stopping — But When?o avoid the overfitting (“early stopping”). The exact criterion used for validation-based early stopping, however, is usually chosen in an ad-hoc fashion or training is stopped interactively. This trick describes how to select a stopping criterion in a systematic fashion; it is a trick for either sp作者: 流行 時間: 2025-3-25 14:04 作者: 祖?zhèn)髫敭a(chǎn) 時間: 2025-3-25 18:05 作者: 單片眼鏡 時間: 2025-3-25 22:43 作者: sterilization 時間: 2025-3-26 02:57
Large Ensemble Averaging an infinite ensemble of predictors from finite (small size) ensemble information. We demonstrate it on ensembles of networks with different initial choices of synaptic weights. We find that the optimal stopping criterion for large ensembles occurs later in training time than for single networks. We作者: sebaceous-gland 時間: 2025-3-26 05:28 作者: 招人嫉妒 時間: 2025-3-26 09:21
A Dozen Tricks with Multitask Learningning signals of other . tasks. It does this by learning the extra tasks in parallel with the main task while using a shared representation; what is learned for each task can help other tasks be learned better. This chapter describes a dozen opportunities for applying multitask learning in real probl作者: Noctambulant 時間: 2025-3-26 14:37 作者: 容易生皺紋 時間: 2025-3-26 19:01 作者: 勤勞 時間: 2025-3-26 21:45
Avoiding Roundoff Error in Backpropagating Derivativesuts. The roundoff error can lead result in high relative error in derivatives, and in particular, derivatives being calculated to be zero when in fact they are small but non-zero. This roundoff error is easily avoided with a simple programming trick which has a small memory overhead (one or two extr作者: hedonic 時間: 2025-3-27 03:03
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagationand computational resources are unlimited, even trivial algorithms will converge to the optimal solution. However, in the practical case, given limited data and other resources, satisfactory performance requires sophisticated methods to regularize the problem by introducing . knowledge. Invariance o作者: Mhc-Molecule 時間: 2025-3-27 08:08 作者: SUE 時間: 2025-3-27 10:33
Neural Network Classification and Prior Class Probabilities - if the number of training examples that correspond to each class varies significantly between the classes, then it may be harder for the network to learn the rarer classes in some cases. Such practical experience does not match theoretical results which show that MLPs approximate Bayesian . proba作者: 友好關系 時間: 2025-3-27 13:41
.The only book that looks at prosocial bahavior from a socioThis book is the product of an intensive cooperation between psych- ogists and sociologists who study solidarity and prosocial behavior, and its fruits are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are作者: 脾氣暴躁的人 時間: 2025-3-27 18:39
Klaus-Robert Müllers are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are at the core of both disciplines and thus one might expect that an intensive cooperation like the one that produced this book is not uncommon. Surprisingly however, it is extremely rare that sociologists and ps作者: Loathe 時間: 2025-3-27 23:02 作者: faucet 時間: 2025-3-28 05:03 作者: 星星 時間: 2025-3-28 08:21 作者: 巨頭 時間: 2025-3-28 13:02
Lutz Prechelt.The only book that looks at prosocial bahavior from a socioThis book is the product of an intensive cooperation between psych- ogists and sociologists who study solidarity and prosocial behavior, and its fruits are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are作者: Foregery 時間: 2025-3-28 16:02 作者: entreat 時間: 2025-3-28 20:49 作者: 蛛絲 時間: 2025-3-29 01:45
Jan Larsen,Claus Svarer,Lars Nonboe Andersen,Lars Kai Hansen.The only book that looks at prosocial bahavior from a socioThis book is the product of an intensive cooperation between psych- ogists and sociologists who study solidarity and prosocial behavior, and its fruits are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are作者: Minutes 時間: 2025-3-29 04:25 作者: 小隔間 時間: 2025-3-29 08:14 作者: fatuity 時間: 2025-3-29 13:45
Gary William Flakes are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are at the core of both disciplines and thus one might expect that an intensive cooperation like the one that produced this book is not uncommon. Surprisingly however, it is extremely rare that sociologists and ps作者: 羽飾 時間: 2025-3-29 15:34
Rich Caruanas are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are at the core of both disciplines and thus one might expect that an intensive cooperation like the one that produced this book is not uncommon. Surprisingly however, it is extremely rare that sociologists and ps作者: Esophagitis 時間: 2025-3-29 22:59
Patrick van der Smagt,Gerd Hirzinger.The only book that looks at prosocial bahavior from a socioThis book is the product of an intensive cooperation between psych- ogists and sociologists who study solidarity and prosocial behavior, and its fruits are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are作者: 幸福愉悅感 時間: 2025-3-30 01:47 作者: obsolete 時間: 2025-3-30 06:53
Tony Plate.The only book that looks at prosocial bahavior from a socioThis book is the product of an intensive cooperation between psych- ogists and sociologists who study solidarity and prosocial behavior, and its fruits are briefly summarized in Chapter 1. The topics of so- darity and prosocial behavior are作者: caldron 時間: 2025-3-30 10:47 作者: cogitate 時間: 2025-3-30 12:50 作者: 巨頭 時間: 2025-3-30 18:38 作者: GLIDE 時間: 2025-3-30 22:08
Grégoire Montavon,Geneviève B. Orr,Klaus-Robert MüThe second edition of the book "reloads" the first edition with more tricks.Provides a timely snapshot of tricks, theory and algorithms that are of use作者: Insulin 時間: 2025-3-31 02:13 作者: Asperity 時間: 2025-3-31 06:55 作者: Asseverate 時間: 2025-3-31 09:54
Regularization Techniques to Improve GeneralizationGood tricks for regularization are extremely important for improving the generalization ability of neural networks. The first and most commonly used trick is ., which was originally described in [11].作者: Receive 時間: 2025-3-31 16:47
Improving Network Models and Algorithmic TricksThis section contains 5 chapters presenting easy to implement tricks which modify either the architecture and/or the learning algorithm so as to enhance the network’s modeling ability. Better modeling means better solutions in less time.作者: Lobotomy 時間: 2025-3-31 20:14
Representing and Incorporating Prior Knowledge in Neural Network TrainingThe present section focuses on tricks for four important aspects in learning: (1) incorporation of prior knowledge, (2) choice of representation for the learning task, (3) unequal class prior distributions, and finally (4) large network training.作者: 寡頭政治 時間: 2025-4-1 01:44
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663731.jpg作者: SMART 時間: 2025-4-1 04:51
