Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization. - Aseem Baranwal; Kimon Fountoulakis; Aukosh Jagannath (ICML, 2021)

Effects of Graph Convolutions in Deep Networks. - Aseem Baranwal; Kimon Fountoulakis; Aukosh Jagannath (CoRR, 2022)

Effects of Graph Convolutions in Multi-layer Networks. - Aseem Baranwal; Kimon Fountoulakis; Aukosh Jagannath (ICLR, 2023)

Optimality of Message-Passing Architectures for Sparse Graphs. - Aseem Baranwal; Kimon Fountoulakis; Aukosh Jagannath (NeurIPS, 2023)

Graph Attention Retrospective. - Kimon Fountoulakis; Amit Levi; Shenghao Yang 002; Aseem Baranwal; Aukosh Jagannath (J. Mach. Learn. Res., 2023)

Leveraging Generative Pre-Trained Transformer Models for Standardizing Nursing Data. - Aseem Baranwal; Alexander Semenov; Patricia De Oliveira Salgado; Karen B. Priola; Yingwei Yao; Gail M. Keenan; Tamara Goncalves Rezende Macieira (ICHI, 2024)

Analysis of Corrected Graph Convolutions. - Robert Wang 004; Aseem Baranwal; Kimon Fountoulakis (NeurIPS, 2024)