Parameter prediction is essential for many applications, facilitating
insightful interpretation and decision-making. However, in many real life
domains, such as power systems, medicine, and engineering, it can be very
expensive to acquire ground truth labels for certain datasets as they may
require extensive and expensive laboratory testing. In this work, we introduce
a semi-supervised learning approach based on topological projections in
self-organizing maps (SOMs), which significantly reduces the required number of
labeled data points to perform parameter prediction, effectively exploiting
information contained in large unlabeled datasets. Our proposed method first
trains SOMs on unlabeled data and then a minimal number of available labeled
data points are ultimately assigned to key best matching units (BMU). The
values estimated for newly-encountered data points are computed utilizing the
average of the $n$ closest labeled data points in the SOM's U-matrix in tandem
with a topological shortest path distance calculation scheme. Our results
indicate that the proposed semi-supervised model significantly outperforms
traditional regression techniques, including linear and polynomial regression,
Gaussian process regression, K-nearest neighbors, as well as various deep
neural network models.

我们提出了一种基于自组织映射中的拓扑投射的半监督学习方法，通过利用大规模未标记数据集中的信息，显著减少了进行参数预测所需的标记数据点数量，从而有效降低了在某些领域（如电力系统、医学和工程学）中获取地面实况标记的成本。

自组织映射中的拓扑投影最小监督学习

Minimally Supervised Learning using Topological Projections in  Self-Organizing Maps

Deep neural networks (DNN) have achieved remarkable success in various
fields, including computer vision and natural language processing. However,
training an effective DNN model still poses challenges. This paper aims to
propose a method to optimize the training effectiveness of DNN, with the goal
of improving model performance. Firstly, based on the observation that the DNN
parameters change in certain laws during training process, the potential of
parameter prediction for improving model training efficiency and performance is
discovered. Secondly, considering the magnitude of DNN model parameters,
hardware limitations and characteristics of Stochastic Gradient Descent (SGD)
for noise tolerance, a Parameter Linear Prediction (PLP) method is exploit to
perform DNN parameter prediction. Finally, validations are carried out on some
representative backbones. Experiment results show that compare to the normal
training ways, under the same training conditions and epochs, by employing
proposed PLP method, the optimal model is able to obtain average about 1%
accuracy improvement and 0.01 top-1/top-5 error reduction for Vgg16, Resnet18
and GoogLeNet based on CIFAR-100 dataset, which shown the effectiveness of the
proposed method on different DNN structures, and validated its capacity in
enhancing DNN training efficiency and performance.

提出了一种优化深度神经网络训练效果的方法，通过参数预测来改进模型性能，通过实验证实了该方法在不同的深度神经网络结构上提高了训练效率和性能。

通过线性预测提高深度神经网络训练效率和性能

Enhancing Deep Neural Network Training Efficiency and Performance  through Linear Prediction

In this paper, we introduce the new ideas of augmenting Convolutional Neural
Networks (CNNs) with Memory and learning to learn the network parameters for
the unlabelled images on the fly in one-shot learning. Specifically, we present
Memory Matching Networks (MM-Net) --- a novel deep architecture that explores
the training procedure, following the philosophy that training and test
conditions must match. Technically, MM-Net writes the features of a set of
labelled images (support set) into memory and reads from memory when performing
inference to holistically leverage the knowledge in the set. Meanwhile, a
Contextual Learner employs the memory slots in a sequential manner to predict
the parameters of CNNs for unlabelled images. The whole architecture is trained
by once showing only a few examples per class and switching the learning from
minibatch to minibatch, which is tailored for one-shot learning when presented
with a few examples of new categories at test time. Unlike the conventional
one-shot learning approaches, our MM-Net could output one unified model
irrespective of the number of shots and categories. Extensive experiments are
conducted on two public datasets, i.e., Omniglot and \emph{mini}ImageNet, and
superior results are reported when compared to state-of-the-art approaches.
More remarkably, our MM-Net improves one-shot accuracy on Omniglot from 98.95%
to 99.28% and from 49.21% to 53.37% on \emph{mini}ImageNet.

本文提出了 Memory Matching Networks（MM-Net）的新想法，即在卷积神经网络中加入记忆元素，并学习动态地适应无标签图像的参数，从而实现一次学习。文章探讨了不同的训练数据集的匹配方法，并得出了超越同类算法的实验结果。