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Neural Network Memorization Dissection
Jindong Gu, Volker Tresp
Deep neural networks (DNNs) can easily fit a random labeling of the training data with zero training error. What is the difference between DNNs trained with random labels and the o…
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk +2
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model fo…
Improving the Robustness of Capsule Networks to Image Affine Transformations
Jindong Gu, Volker Tresp
Convolutional neural networks (CNNs) achieve translational invariance by using pooling operations. However, the operations do not preserve the spatial relationships in the learned…
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck
Ilja Manakov, Markus Rohm, Volker Tresp
In this paper, we present an in-depth investigation of the convolutional autoencoder (CAE) bottleneck. Autoencoders (AE), and especially their convolutional variants, play a vital…
Semantics for Global and Local Interpretation of Deep Neural Networks
Jindong Gu, Volker Tresp
Deep neural networks (DNNs) with high expressiveness have achieved state-of-the-art performance in many tasks. However, their distributed feature representations are difficult to i…
Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders
Ilja Manakov, Volker Tresp
In this paper, we focus on the problem of identifying semantic factors of variation in large image datasets. By training a convolutional Autoencoder on the image data, we create en…