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Frederic Gibou, Luis Ángel Larios Cárdenas - 2020
We propose a deep learning strategy to compute the mean curvature of an implicit level-set representation of an interface. Our approach is based on fitting neural networks to synthetic datasets of pairs of nodal $\phi$ values and curvatures obtained from circular interfaces immersed in different uniform resolutions. These neural networks are mult...
Axel-Cyrille Ngonga Ngomo, Caglar Demir - 2020
Knowledge graph embedding methods learn continuous vector representations for entities in knowledge graphs and have been used successfully in a large number of applications. We present a novel and scalable paradigm for the computation of knowledge graph embeddings, which we dub PYKE . Our approach combines a physical model based on Hooke's law an...
Sarunas Girdzijauskas, Zekarias T. Kefato - 2020
Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-fr...
Volker Tresp, Mitchell Joblin, Martin Ringsquandl, Yunpu Ma, Jorge Andres Quintero Serna, Marcel Hildebrandt - 2020
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being fa...
Dingding Wang, Goce Trajcevski, Feng Liu, Xiaofeng Zhu - 2020
Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one appealing property is the addi...
Colin Raffel, Han Zhang, Alex Kurakin, Ekin D. Cubuk, Nicholas Carlini, Zizhao Zhang, Chun-Liang Li, David Berthelot, Kihyuk Sohn - 2020
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on...
Su-In Lee, Alex J. DeGrave, Gabriel Erion, Joseph D. Janizek - 2020
While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same...
Zuoyong Li, Fengbin Zhang, Haoyi Fan - 2020
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure...
Miltiadis Allamanis, Ajitha Rajan, Foivos Tsimpourlas - 2020
The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing. The goal in this paper is to solve the test oracle problem in a way that is general, scalable and accurate. To achieve this, we use supervised learning over test ex...
Jan Peters, Hany Abdulsamad, Joao Carvalho, Samuele Tosatto - 2020
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The price of such inefficiency becomes evident in real...
