As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.

Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.

As the performance of computing devices such as graphics processing units (GPUs) has improved dramatically, many deep neural network models, especially convolutional neural networks (CNNs), have been widely applied to various applications such as image classification, semantic segmentation, and object recognition. However, effective first-order optimization methods for CNNs have rarely been studied, although many CNN models have been successfully developed. Accordingly, this paper investigates various advanced adaptive solution search methods and proposes a new first-order optimization algorithm for CNNs called Adam-ASC. Our approach uses four sophisticated adaptive solution search methods to adjust its search strength in the complicated large-dimensional weight solution space spanned by a loss function. At the same time, we explain how they can be combined compensatively to form a complete optimizer with a detailed implementation. From the experiments, we found that our Adam-ASC can significantly improve the image recognition performance of practical CNNs in both the image classification and segmentation tasks. These experimental results show that the four fundamental methods of Adam-ASC and their compensative combination strategy play a crucial role in training CNNs by effectively finding their optimal weights.

비선형 함수의 전역 최적해를 위한 협력 공진화 알고리즘에서 해 탐색 성능을 강화하기 위해서는 제한된 비용 내에서 목적 함수의 적합도 향상에 가장 큰 영향을 미치는 부분 문제를 발견하고, 해당 부분 문제에 대해 집중적으로 해 탐색을 수행하는 것이 가장 효과적이다. 동시에, 해 탐색의 다양성을 보장하고 조기 수렴을 예방하기 위해서는 여러 부분 문제에 대한 탐색 또한 주기적으로 병행해야 한다. 이에 따라, 본 논문에서는 협력 공진화(CC, Cooperative Coevolution) 알고리즘에서 부분 문제의 선택과 효과적인 최적해 탐색을 위해 MAB(Multi-Armed Bandit) 알고리즘을 활용하는 MAB 기반의 협력 공진화 알고리즘인 MABCC(MAB-based CC)를 제안한다. 실제 1000차원의 비선형 벤치마크 함수를 사용하여 실험을 진행한 결과, 제안하는 MAB 알고리즘 기반의 협력 공진화 알고리즘이 기존에 제안된 협력 공진화 알고리즘보다 더욱 우수한 최적해 탐색 성능을 가짐을 확인하였다.