Cloutier, “Learning a synaptic learning rule,” in Proc. Int. Joint Conf. Neural Netw., 1990, p. 969. S. Thrun and L. Pratt, “Learning to learn: Introduction and overview,”in, Learning to Learn. Boston, MA, USA: Springer, 1998. R. Vilalta and Y. Drissi, “A perspective view ...
B. R. Vuorio, S.-H. Sun, H. Hu, and J. J. Lim, “Multimodal Model-Agnostic Meta-Learning Via Task-Aware Modulation,” in NeurIPS, 2019. 根据不同的任务选取不同的prior,设计了两个网络,第一个网络用来提取当前任务的类型,将其编码为表征加入到task network层级的参数中。这和c-glow中cond的...
ALVINN (Autonomous Land Vehicle In a Neural Network)是一个基于神经网络的智能系统,通过观察人类的驾驶来学习驾驶,ALVINN能够控制NavLab,装在一辆改装版军用悍马,这辆悍马装载了传感器、计算机和驱动器用来进行自动驾驶的导航试验。实现ALVINN功能的第一步,是对它进行训练,也就是训练一个人驾驶汽车。 然后让ALVINN观看...
在机器学习中，重要性采样和AdaBoost算法都尝试通过采样的方法改变数据分布。如果采样的数据分布与验证数据...
ALVINN (Autonomous Land Vehicle In a Neural Network)是一个基于神经网络的智能系统, 通过观察人类的驾驶来学习驾驶,ALVINN 能够控制 NavLab,装在一辆改装版军用悍马,这辆悍马装载了传感器、计算机和驱动器用来进行自动驾驶的导航试验。实现 ALVINN 功能的 第一步,是对它进行训练,也就是训练一个人驾驶汽车。
Noisy spiking neural network and noise-driven learning 在这篇文章中,我们考虑了一种噪声泄漏积分和发放(LIF)脉冲神经元模型(见实验过程方法详细信息),遵循了先前使用扩散近似的文献30,31,48。它考虑了一个离散的亚阈值公式,其形式为: 其中,u表示膜电压,xt表示时间 t 的输入,τ是膜时间常数,而Φθ是参数化输...
Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neur...
classNetwork(object):def__init__(self,sizes):self.num_layers=len(sizes)self.sizes=sizes self.biases=[np.random.randn(y,1)foryinsizes[1:]]self.weights=[np.random.randn(y,x)forx,yinzip(sizes[:-1],sizes[1:])] TODO: 32 参考 ...
Neural networks approach the problem in a different way. The idea is to take a large number of handwritten digits, known as training examples, and then develop a system which can learn from those training examples. In other words, the neural network uses the examples to automatically infer rul...
transpose()) return (nabla_b, nabla_w) def evaluate(self, test_data): """Return the number of test inputs for which the neural network outputs the correct result. Note that the neural network's output is assumed to be the index of whichever neuron in the final layer has the highest ...