本文是我个人认为Hinton最有潜力的论文之一:《Forward-Forward算法:一些初步探究》 文章正文 一、该论文希望解决什么问题 该论文旨在解决当前反向传播(Backpropagation, BP)算法在神经网络训练中的局限性,特别是其在生物学上的不可行性和处理序列数据时的低效。具体来说,作者希望通过引入一种新的学习过程——Forward-For...
The Forward-Forward Algorithm Frank Cao 华中科技大学 工学硕士 来自专栏 · 深度学习 这是Hinton继胶囊模型后在AI底层算法模式创新的又一次尝试,该模型最大的特别之处在于:相对经典的神经网络直接删除了后向反馈过程(经典神经网络分为正向传播和后向反馈两个相对独立的过程),仅使用正向传播过程;对于经典神经网络...
摘要 这篇论文的目的就是为深度学习神经网络,引入了一种新的学习过程,并且在一些小的问题中进行尝试,效果很不错,值得再深入探究下去。前向正演算法用了两个前向传播代替了反向传播算法的前向传播和反向传播,这两次前向传播喂进去的数据样例分别为一正一负。网络的每一层都有自己的目标函数,并且对正样例有积极影响...
Computational Benefits of the Predictive Forward-Forward Algorithm: From a hardware efficiency point-ofview, the PFF algorithm, much like the FF procedure5, is a potentially promising candidate for implementation in analog and neuromorphic hardware. It is the fact that FF and PFF only require forwa...
The "forward-backward" algorithm, originally developed for estimation of a posteriori probabilities of individual symbols in digital sequences, has a variety of generalizations that are crucial for such "hot" current applications as decoding "turbo codes," low-density parity-check codes, and tail-...
Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising ...
由于直接暴力计算 𝒑(𝒛│𝒙) 的复杂度非常高,作者借鉴 HMM 的 Forward-Backward 算法思路,利用动态规划算法求解。 如下图,为了更形象表示问题的搜索空间,用 X 轴表示时间序列, Y 轴表示输出序列,并把输出序列做标准化处理,输出序列中间和头尾都加上 blank,用l表示最终标签,l’表示扩展后的形式,则由 2|...
An efficient algorithm for computing the forward chirp z-transform was described 50 years ago1,2,3,4,5. It was derived using an index substitution, which was originally proposed by Bluestein1,5, to compute the transform using fast convolution. It runs in O(nlogn) time, where n is the ...
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Algorithm S5 in Supplementary Section S2 gives the pseudo-code for the forward CZT. Inverse CZT In the square case, i.e., when M = N, the ICZT can be stated10 by inverting Eq. (7), which leads to: $${\bf{x}}={{\bf{A}}}^{-1}{{\bf{Q}}}^{-1}\ {{\boldsymbol{{\hat...