Deep Set Prediction Networks

FSPool: Learning set representations with featurewise sort pooling

Original

Zhang, Y., Hare, J., and Prügel-Bennett, A · 1906

Earlier work this paper cites.

Faster R-CNN: Towards real-time object detection with region proposal networks

Ren, S., He, K., Girshick, R., and Sun, J · 2015

Earlier work this paper cites.

Order Matters: Sequence to sequence for sets

Vinyals, O., Bengio, S., and Kudlur, M · 2015

Earlier work this paper cites.

Structured prediction energy networks

Belanger, D. and McCallum, A · 2016

Earlier work this paper cites.

Deep residual learning for image recognition

Original

He, K., Zhang, X., Ren, S., and Sun, J · 2016

Earlier work this paper cites.

End-to-end people detection in crowded scenes

Stewart, R. and Andriluka, M · 2016

Earlier work this paper cites.

End-to-end learning for structured prediction energy networks

Belanger, D., Yang, B., and McCallum, A · 2017

Earlier work this paper cites.

A point set generation network for 3D object reconstruction from a single image

Fan, H., Su, H., and Guibas, L. J · 2017

Earlier work this paper cites.

CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning

Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R · 2017

Earlier work this paper cites.

Introspective neural networks for generative modeling

Lazarow, J., Jin, L., and Tu, Z · 2017

Earlier work this paper cites.

Automatic differentiation in PyTorch

Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017

Cited alongside, same era.

A simple neural network module for relational reasoning

Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017

Cited alongside, same era.

Attention is all you need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017

Cited alongside, same era.

Deep Sets

Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017

Cited alongside, same era.

Learning representations and generative models for 3D point clouds

Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L. J · 2018

Cited alongside, same era.

Optimizing the latent space of generative networks

Bojanowski, P., Joulin, A., Paz, D. L., and Szlam, A · 2018

Cited alongside, same era.

Learning Latent Permutations with Gumbel-Sinkhorn Networks

Mena, G., Belanger, D., Linderman, S., and Snoek, J · 2018

Later among the works it cites.

Concept learning with energy-based models

Original

Mordatch, I · 2018

Later among the works it cites.

Deep perm-set net: Learn to predict sets with unknown permutation and cardinality using deep neural networks

Original

Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., and Reid, I · 2018

Later among the works it cites.

GraphVAE: Towards generation of small graphs using variational autoencoders

Simonovsky, M. and Komodakis, N · 2018

Later among the works it cites.

FoldingNet: Point cloud auto-encoder via deep grid deformation

Yang, Y., Feng, C., Shen, Y., and Tian, D · 2018

Later among the works it cites.