Paul Soulos

I am a PhD student studying Computational Cognitive Science at Johns Hopkins University and a collaborating researcher with the Microsoft Research Deep Learning Group. My main areas of focus are compositionality, generalization, and disentangled representations in neural networks. My PhD advisors are Paul Smolensky and Leyla Isik.

Before entering graduate school, I worked as a software engineer at Google and Fitbit. My focus at these companies was on wearables and health technology.

CV / GitHub / Google Scholar

Research

My work intersects Cognitive Science, Computational Linguistics, Machine Learning, and Neuroscience.

	Differentiable Tree Operations Promote Compositional Generalization Paul Soulos, Edward Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao Proceedings of the 40th International Conference on Machine Learning, 2023 arxiv / paper / code / poster /
	Disentangled Face Representations in Humans and Machines Paul Soulos, Leyla Isik Conference on Cognitive Computational Neuroscience, 2022 arxiv / code / poster /
	Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages Paul Soulos, Sudha Rao, Caitlin Smith, Eric Rosen, Asli Celikyilmaz, R. Thomas McCoy, Yichen Jiang, Coleman Haley, Roland Fernandez, Hamid Palangi, Jianfeng Gao, Paul Smolensky Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021), 2021 arxiv /
	Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021 arxiv /
	Discovering the Compositional Structure of Vector Representations with Role Learning Networks Paul Soulos, R. Thomas McCoy, Tal Linzen, Paul Smolensky Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020 arxiv / code /
	Learning to generalize like humans using basic-level object labels Joshua C Peterson, Paul Soulos, Aida Nematzadeh, Thomas L Griffiths Journal of Vision, 2019 arxiv /

Design and source code from Jon Barron's website

Paul Soulos

Research

Differentiable Tree Operations Promote Compositional Generalization

Disentangled Face Representations in Humans and Machines

Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

Discovering the Compositional Structure of Vector Representations with Role Learning Networks

Learning to generalize like humans using basic-level object labels