Hypernetwork Knowledge Graph Embeddings

Nickel, M., Tresp, V., Kriegel, H.P.: A Three-Way Model for Collective Learning on Multi-Relational Data. In: International Conference on Machine Learning (2011)

2011

Earlier work this paper cites.

Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating Embeddings for Modeling Multi-relational Data. In: Advances in Neural Information Processing Systems (2013)

2013

Earlier work this paper cites.

Mahdisoltani, F., Biega, J., Suchanek, F.M.: Yago3: A Knowledge Base from Multilingual Wikipedias. In: Conference on Innovative Data Systems Research (2013)

2013

Earlier work this paper cites.

Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with Neural Tensor Networks for Knowledge Base Completion. In: Advances in Neural Information Processing Systems (2013)

2013

Earlier work this paper cites.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15

2014

Earlier work this paper cites.

Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: International Conference on Machine Learning (2015)

2015

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. In: International Conference on Learning Representations (2015)

2015

Earlier work this paper cites.

Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing Text for Joint Embedding of Text and Knowledge Bases. In: Empirical Methods in Natural Language Processing (2015)

2015

Earlier work this paper cites.

Yang, B., Yih, W.t., He, X., Gao, J., Deng, L.: Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In: International Conference on Learning Representations (2015)

2015

Cited alongside, same era.

Nickel, M., Rosasco, L., Poggio, T.A.: Holographic Embeddings of Knowledge Graphs. In: Association for the Advancement of Artificial Intelligence (2016)

2016

Cited alongside, same era.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision. In: Computer Vision and Pattern Recognition (2016)

2016

Cited alongside, same era.

Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex Embeddings for Simple Link Prediction. In: International Conference on Machine Learning (2016)

2016

Cited alongside, same era.

Ha, D., Dai, A., Le, Q.V.: Hypernetworks. In: International Conference on Learning Representations (2017)

Yang, F., Yang, Z., Cohen, W.W.: Differentiable Learning of Logical Rules for Knowledge Base Reasoning. In: Advances in Neural Information Processing Systems (2017)

Original

2017

Later among the works it cites.

Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A., McCallum, A.: Go for a Walk and Arrive at the Answer: Reasoning over Paths in Knowledge Bases Using Reinforcement Learning. In: International Conference on Learning Representations (2018)

2018

Closest in time.

Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D Knowledge Graph Embeddings. In: Association for the Advancement of Artificial Intelligence (2018)

2018

Closest in time.

Ebisu, T., Ichise, R.: TorusE: Knowledge Graph Embedding on a Lie Group. In: Association for the Advancement of Artificial Intelligence (2018)

2018

Closest in time.