Predicate Classification Using Optimal Transport Loss in Scene Graph Generation

In scene graph generation (SGG), learning with cross-entropy loss yields biased predictions owing to the severe imbalance in the distribution of the relationship labels in the dataset. Thus, this study proposes a method to generate scene graphs using optimal transport as a measure for comparing two probability distributions. We apply learning with the optimal transport loss, which reflects the similarity between the labels in terms of transportation cost, for predicate classification in SGG. In the proposed approach, the transportation cost of the optimal transport is defined using the similarity of words obtained from the pre-trained model. The experimental evaluation of the effectiveness demonstrates that the proposed method outperforms existing methods in terms of mean Recall@50 and 100. Furthermore, it improves the recall of the relationship labels scarcely available in the dataset

RoboCup Soccer Leagues

RoboCup was created in 1996 by a group of Japanese, American, and European Artificial Intelligence and Robotics researchers with a formidable, visionary long-term challenge: “By 2050 a team of robot soccer players will beat the human World Cup champion team.” At that time, in the mid 90s, when there were very few effective mobile robots and the Honda P2 humanoid robot was presented to a stunning public for the first time also in 1996, the RoboCup challenge, set as an adversarial game between teams of autonomous robots, was fascinating and exciting. RoboCup enthusiastically and concretely introduced three robot soccer leagues, namely “Simulation,” “Small-Size,” and “Middle-Size,” as we explain below, and organized its first competitions at IJCAI’97 in Nagoya with a surprising number of 100 participants [RC97]. It was the beginning of what became a continously growing research community. RoboCup established itself as a structured organization (the RoboCup Federation www.RoboCup.org). RoboCup fosters annual competition events, where the scientific challenges faced by the researchers are addressed in a setting that is attractive also to the general public. and the RoboCup events are the ones most popular and attended in the research fields of AI and Robotics.RoboCup further includes a technical symposium with contributions relevant to the RoboCup competitions and beyond to the general AI and robotics

A Model of Recurrent Neural Networks that Learn State-Transitions of Finite State Transducers

A model, called the `SGH' model, and its learning method are proposed. While simple recurrent networks (SRN) and finite state transducers (FST) have similar structures, their learning are quite different, so that SRNs can not acquire suitable state-transitions through conventional learning. The proposed model and method construct an SRN that has suitable state-transitions for a given task. In order to derive the model and the method, a procedure to construct an FST from examples of inputoutput is composed using the state-minimization technique. This procedure consists of three steps, the `keeping input history' step, the `grouping states' step, and the `constructing state-transitions' step. Then each step is reconstructed as learning of a neural network. Finally, three networks are combined into the `SGH' model. Experiments show that the `SGH' model can learn suitable state transitions for given tasks. Experiments also show that it increases the ability to process temporal sequences wi..

情報の記号的・構造的表現を学習する神経回路網

本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・課程博士博士(工学)甲第5860号工博第1404号新制||工||978(附属図書館)UT51-95-B205京都大学大学院工学研究科電気工学専攻(主査)教授 長尾 真, 教授 池田 克夫, 教授 矢島 脩三学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDFA

Explainable Recommendation Using Knowledge Graphs and Random Walks

A knowledge graph (KG) contains rich information about users and items. The relationship among users and items can help to generate intuitive explanations for recommended items. Many variations of KG-based recommendation algorithms use the shortest path from the user to the item in order to generate an explanation of the recommendation. However, the simple shortest path may not be useful in the case when the path is long, because the interpretation of the long path is difficult. Also, there may be no path between the user and the recommended item. In order to overcome these difficulties, we proposed an extension of the existing framework based on random walk with KG embedding. In the proposed framework, we use the most probable path in a random walk as an explanation. Thereby, our framework can even explain items that have no connection in the KG due to the latent connection resulting from random walk teleportation. Comparison experiment demonstrated that the framework can provide more suitable recommendations than the existing method. In addition, the experiment show the ability of the proposed method to generate explanation for all recommendations that have no path in the graph.2022 IEEE International Conference on Big Data (Big Data). 17-20 December 2022, Osaka International Convention Center (OICC),Osaka

Neural Networks that Learn Symbolic and Structured Representation of Information

In the research of artificial intelligence, symbol processing has been providing powerful tools to represent and process complicated and varied information. However, it has disadvantages in `analogy', `uncertainty', and `learning/adapting'. Neural networks have been expected to conquer these disadvantages of symbol processing. In order to provide flexible and robust problem solving methods, many researchers have been trying to construct hybrid or integrated systems of symbol and neural processing. However, there remains a problem in such a hybrid way, which comes from difference of characteristics of data that neural and symbol processings deal with. Especially, following two essential characteristics of data representation in symbol processing are important. ffl Symbols: each of which indicates discrete and independent information. ffl Data structures: by which complicated information is arranged flexibly. Because neural networks originally have not mechanisms to deal with these chara..

Team GAMMA: Agent Programming on Gaea

this paper we describe an application of organic programming language, Gaea, to the programming of players of a multi-agent game, soccer. Our approach to multiagent systems is recursive. Each agent is programmed in a multiagent way. In programing a complex agent like soccer player, we need various kinds of mechanisms of mode-change, interruption and emergency exit. We propose "dynamic subsumption architecture" as a flexible methodology to realize such mechanisms. 2 Gae