Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. - Joshua C. Peterson; Paul Soulos; Aida Nematzadeh; Tom Griffiths 001 (CogSci, 2018)

Discovering the Compositional Structure of Vector Representations with Role Learning Networks. - Paul Soulos; R. Thomas McCoy; Tal Linzen; Paul Smolensky (BlackboxNLP@EMNLP, 2020)

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 001 (NAACL-HLT, 2021)

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 001; Paul Smolensky (CoRR, 2022)

Differentiable Tree Operations Promote Compositional Generalization. - Paul Soulos; Edward J. Hu; Kate McCurdy; Yunmo Chen; Roland Fernandez; Paul Smolensky; Jianfeng Gao 001 (ICML, 2023)

Toward Compositional Behavior in Neural Models: A Survey of Current Views. - Kate McCurdy; Paul Soulos; Paul Smolensky; Roland Fernandez; Jianfeng Gao 001 (EMNLP, 2024)

Compositional Generalization Across Distributional Shifts with Sparse Tree Operations. - Paul Soulos; Henry Conklin; Mattia Opper; Paul Smolensky; Jianfeng Gao 001; Roland Fernandez (NeurIPS, 2024)

Disentangled deep generative models reveal coding principles of the human face processing network. - Paul Soulos; Leyla Isik (PLoS Comput. Biol., 2024)