`

Timezone: »

 
Spotlight
Reducing the Rank in Relational Factorization Models by Including Observable Patterns
Maximilian Nickel · Xueyan Jiang · Volker Tresp

Wed Dec 10 12:30 PM -- 12:50 PM (PST) @ Level 2, room 210

Tensor factorizations have become popular methods for learning from multi-relational data. In this context, the rank of a factorization is an important parameter that determines runtime as well as generalization ability. To determine conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover adjacency tensors. Based on our findings, we propose a novel additive tensor factorization model for learning from latent and observable patterns in multi-relational data and present a scalable algorithm for computing the factorization. Experimentally, we show that the proposed approach does not only improve the predictive performance over pure latent variable methods but that it also reduces the required rank --- and therefore runtime and memory complexity --- significantly.

Author Information

Maximilian Nickel (Meta)
Xueyan Jiang (Ludwig-Maximilians-Universität München)
Volker Tresp (Siemens AG)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors

  • 2021 : Towards Data-Free Domain Generalization »
    Ahmed Frikha · Haokun Chen · Denis Krompaß · Thomas Runkler · Volker Tresp
  • 2020 Poster: Riemannian Continuous Normalizing Flows »
    Emile Mathieu · Maximilian Nickel
  • 2019 : Poster Session »
    Rishav Chourasia · Yichong Xu · Corinna Cortes · Chien-Yi Chang · Yoshihiro Nagano · Tiffany Min · Benedikt Boecking · Vu Tran · Seyed Kamyar Seyed Ghasemipour · Qianggang Ding · Shouvik Mani · Vikram Voleti · Rasool Fakoor · Miao Xu · Kenneth Marino · Lisa Lee · Volker Tresp · Jean-Francois Kagy · Marvin Zhang · Barnabas Poczos · Dinesh Khandelwal · Adrien Bardes · Evan Shelhamer · Jiacheng Zhu · Ziming Li · Xiaoyan Li · Dmitrii Krasheninnikov · Ruohan Wang · Mayoore Jaiswal · Emad Barsoum · Suvansh Sanjeev · A Wattanavekin · Qizhe Xie · Sifan Wu · Yuki Yoshida · David Kanaa · Sina Khoshfetrat Pakazad · Mehdi Maasoumy
  • 2019 Poster: Hyperbolic Graph Neural Networks »
    Qi Liu · Maximilian Nickel · Douwe Kiela
  • 2018 : Spotlights »
    Guangneng Hu · Ke Li · Aviral Kumar · Vu Tran · Samuel Fadel · Rita Kuznetsova · Bong-Nam Kang · Behrouz Haji Soleimani · Jinwon An · Nathan de Lara · Anjishnu Kumar · Tillman Weyde · Melanie Weber · Kristen Altenburger · Saeed Amizadeh · Xiaoran (Sean) Xu · Yatin Nandwani · Yang Guo · Maria Pacheco · Liam Fedus · Guillaume Jaume · Yuka Yoneda · Yunpu Ma · Yunsheng Bai · Berk Kapicioglu · Maximilian Nickel · Fragkiskos Malliaros · Beier Zhu · Aleksandar Bojchevski · Joshua Joseph · Gemma Roig · Esma Balkir · Xander Steenbrugge
  • 2017 : Learning Hierarchical Representations of Relational Data »
    Maximilian Nickel
  • 2016 Workshop: Learning with Tensors: Why Now and How? »
    Anima Anandkumar · Rong Ge · Yan Liu · Maximilian Nickel · Rose Yu
  • 2015 Symposium: Brains, Minds and Machines »
    Gabriel Kreiman · Tomaso Poggio · Maximilian Nickel
  • 2006 Poster: Gaussian Process Models for Discriminative Link Prediction »
    Kai Yu · Wei Chu · Shipeng Yu · Volker Tresp · Zhao Xu