Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I

s of Invited Talks Programming by Input-Output Examples

[1]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[2]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[3]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[4]  Aaron C. Courville,et al.  Adversarially Learned Inference , 2016, ICLR.

[5]  Charu C. Aggarwal,et al.  Signed Network Embedding in Social Media , 2017, SDM.

[6]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[7]  Zhao Chen,et al.  Ranking Users in Social Networks With Higher-Order Structures , 2018, AAAI.

[8]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[9]  Dik Lun Lee,et al.  Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks , 2017, KDD.

[10]  Nitesh V. Chawla,et al.  Evaluating link prediction methods , 2014, Knowledge and Information Systems.

[11]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[12]  Mohammed J. Zaki,et al.  Arabesque: a system for distributed graph mining , 2015, SOSP.

[13]  Abhishek Kumar,et al.  Variational Inference of Disentangled Latent Concepts from Unlabeled Observations , 2017, ICLR.

[14]  Sergey Levine,et al.  Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings , 2018, ICML.

[15]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[16]  Matthias Hein,et al.  Community detection in networks via nonlinear modularity eigenvectors , 2017, SIAM J. Appl. Math..

[17]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[18]  Rob Brekelmans,et al.  Invariant Representations without Adversarial Training , 2018, NeurIPS.

[19]  John W. Cahn,et al.  Linking anisotropic sharp and diffuse surface motion laws via gradient flows , 1994 .

[20]  Stefano Soatto,et al.  Emergence of Invariance and Disentanglement in Deep Representations , 2017, 2018 Information Theory and Applications Workshop (ITA).

[21]  Ole Winther,et al.  Auxiliary Deep Generative Models , 2016, ICML.

[22]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine-mediated learning.

[23]  Yang Xiang,et al.  SNE: Signed Network Embedding , 2017, PAKDD.

[24]  Yu Zhang,et al.  Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data , 2017, NIPS.

[25]  Christopher J. C. Burges,et al.  Spectral clustering and transductive learning with multiple views , 2007, ICML '07.

[26]  Danai Koutra,et al.  Scalable Hashing-Based Network Discovery , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[27]  Ping Li,et al.  Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search , 2014, ICML.

[28]  Christopher Burgess,et al.  beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.

[29]  Joshua B. Tenenbaum,et al.  Deep Convolutional Inverse Graphics Network , 2015, NIPS.

[30]  David Vázquez,et al.  PixelVAE: A Latent Variable Model for Natural Images , 2016, ICLR.

[31]  Yoshua Bengio,et al.  A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.

[32]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[33]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Q. Du,et al.  Modelling and simulations of multi-component lipid membranes and open membranes via diffuse interface approaches , 2006, Journal of Mathematical Biology.

[35]  Jure Leskovec,et al.  Local Higher-Order Graph Clustering , 2017, KDD.

[36]  Xiaoming Liu,et al.  Disentangled Representation Learning GAN for Pose-Invariant Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[38]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[39]  Ryan A. Rossi,et al.  Role Discovery in Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[40]  Yee Whye Teh,et al.  Faithful Inversion of Generative Models for Effective Amortized Inference , 2017, NeurIPS.

[41]  Alex Graves,et al.  Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.

[42]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[43]  Emmanuel Müller,et al.  VERSE: Versatile Graph Embeddings from Similarity Measures , 2018, WWW.

[44]  A Vázquez,et al.  The topological relationship between the large-scale attributes and local interaction patterns of complex networks , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[45]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[46]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[47]  D. Prasad.  Survey of The Problem of Object Detection In Real Images , 2012 .

[48]  Ron J. Weiss,et al.  Unsupervised Speech Representation Learning Using WaveNet Autoencoders , 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[49]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[50]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.