帮我解释一下这段话：The connection growth algorithm greedily activates useful, but currently ‘dormant,’ connections. We incorporate it in the following learning policy: Policy 1: Add a connection w iff it can quickly reduce the value of loss function L. The DNN seed contains only a small fraction of active connections to propagate gradients. To locate the ‘dormant’ connections that can reduce L effectively, we evaluate ∂L/∂w for all the ‘dormant’ connections w (computed either using the whole training set or a large batch). Policy 1 activates ‘dormant’ connections iff they are the most efficient at reducing L. This can also assist with avoiding local minima and achieving higher accuracy [28]. To illustrate this policy, we plot the connections grown from the input to the first layer of LeNet-300-100 [7] (for the MNIST dataset) in Fig. 3. The image center has a much higher grown density than the margins, consistent with the fact that the MNIST digits are centered. From a neuroscience perspective, our connection growth algorithm coincides with the Hebbian theory: “Neurons that fire together wire together [29]." We define the stimulation magnitude of the mth presynaptic neuron in the (l + 1)th layer and the n th postsynaptic neuron in the l th layer as ∂L/∂ul+1 m and x l n , respectively. The connections activated based on Hebbian theory would have a strong correlation between presynaptic and postsynaptic cells, thus a large value of (∂L/∂ul+1 m )x l n . This is also the magnitude of the gradient of L with respect to w (w is the weight that connects u l+1 m and x l n ): |∂L/∂w| = (∂L/∂ul+1 m )x l n (1) Thus, this is mathematically equivalent to Policy 1.

这段话描述了一种神经网络训练策略，即通过激活当前处于“休眠状态”的连接来提高训练效果。该策略被称为“连接增长算法”，其核心思想是在保证训练误差下降的前提下，尽可能地激活那些能够快速降低误差的“休眠连接”。在训练开始时，神经网络中只有少量的活跃连接用于梯度传播，因此需要寻找那些“休眠连接”并计算它们对误差的贡献，以此来确定哪些连接应该被激活。这个策略有助于避免局部最小值，并提高准确性。从神经科学的角度来看，这个策略与赫布理论相吻合，即“一起激活的神经元会相互连接”。在具体实现中，通过计算某个神经元的前后连接之间的梯度大小来确定是否激活该连接。这个梯度大小也是连接增长算法中用来衡量连接重要性的指标。

相关问题

请帮我翻译一下：The algorithm in Kingma & Ba, 2015 uses such an update in practice, although their analysis does not

和 Welling's paper proposed a novel way for training generative models using stochastic gradient descent.

答案：Kingma 和 Welling 的论文中提出了一种新的使用随机梯度下降训练生成模型的方法的算法。

Write an algorithm that returns the largest and the second largest values in the array s[1], ..., s[n]. Then code it in Java

算法描述：

1. 初始化两个变量 `max` 和 `secondMax` 为数组的第一个元素。
2. 遍历数组从第二个元素开始（index=1到n）：
a. 如果当前元素 `s[index]` 大于 `max`，将 `secondMax` 更新为原来的 `max`，然后将 `max` 更新为当前元素。
b. 否则，如果当前元素大于 `secondMax` 但不大于 `max`，将 `secondMax` 更新为当前元素。
3. 返回 `max` 和 `secondMax`。

以下是该算法的Java实现：

public class Main {
    public static void main(String[] args) {
        int[] s = {5, 9, 3, 7, 2, 10, 8}; // 测试数组
        Pair<Integer, Integer> result = findLargestAndSecondLargest(s);
        System.out.println("最大值: " + result.first);
        System.out.println("次大值: " + result.second);
    }

    public static Pair<Integer, Integer> findLargestAndSecondLargest(int[] arr) {
        if (arr == null || arr.length < 2) {
            throw new IllegalArgumentException("数组长度至少需要2");
        }
        
        int max = arr[0];
        int secondMax = arr[0];

        for (int i = 1; i < arr.length; i++) {
            if (arr[i] > max) {
                secondMax = max;
                max = arr[i];
            } else if (arr[i] > secondMax && arr[i] != max) {
                secondMax = arr[i];
            }
        }

        return new Pair<>(max, secondMax); // 使用Pair类存储结果
    }
}

// 假设有一个Pair类，用于表示有序对
class Pair<K, V> {
    private K first;
    private V second;

    // 构造函数、getter和setter省略
}