The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters.

摘 要 深度随机配置网络（Deep Stochastic Configuration Network, DSCN）是一种增量式随机化学习模型，具 有人为干预程度低、学习效率高和泛化能力强等优点. 但是，面向噪声数据回归与分析时，传统的DSCN 易受到

2023年3月14日 · To quickly build a randomized model, Wang and Li proposed the stochastic configuration network (SCN) model, which can be constructed incrementally using a data-dependent supervisory mechanism to generate the input weights and biases of hidden nodes [4].

2024年10月1日 · Deep stochastic configuration networks (DSCNs) employ data-dependent supervision mechanism to randomly assign node parameters and incrementally construct the deep neural network structure, thereby ensuring the model's universal approximation property.

In contrast to known randomized learning algorithms for single layer feed-forward neural networks (e.g., random vector functional-link networks), Stochastic Configuration Networks (SCNs) randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, while the output weights are analytically evaluated ...

2024年12月5日 · 可变形条带卷积（DSCN）：相比于DCNv3，DSCN的计算负载只有原来的63.2%，并且避免了计算负载随着内核大小的增加而二次方增长的问题，性能提升具体数据体现在推理速度是DCNv3大内核的2.1倍。 可变形空间注意力（DSA）：在保持可变形采样的同时避免了稀疏采样的问题，性能提升具体数据体现在在ADE20K数据集上的语义分割任务中，DSAN-S轻量级解码器的mIoU为48.8%，高于基于DCNv3的InternImage-T重型解码器的结果，而参数数量 …

dscn是直接耦合的差分放大器，由两个输入端（非反相端和反相端）、一个输出端和一个电源端组成。 dscn 的工作原理如下：当输入信号加在非反相端时，通过反馈电阻的作用，将一部分输出信号反馈到反相端。

2023年3月21日 · 深度随机配置网络 (Deep Stochastic Configuration Network,DSCN) 是一种增量式随机化学习模型，具有人为干预程度低、学习效率高和泛化能力强等优点。 但是，面向噪声数据回归与分析时，传统的 DSCN 易受到异常值影响，从而降低了模型的泛化性。

2022年8月19日 · To facilitate the discovery of novel target (combinations), we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

2018年8月7日 · This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data st...