GRACE: Generative Robust Analog Circuit Exploration. - Michael A. Terry; Jonathan Marcus; Matthew Farrell; Varun Aggarwal; Una-May O'Reilly (EvoWorkshops, 2006)

Hyperbolicity, Degeneracy, and Expansion of Random Intersection Graphs. - Matthew Farrell; Timothy D. Goodrich; Nathan Lemons; Felix Reidl; Fernando Sánchez Villaamil; Blair D. Sullivan (WAW, 2015)

Multi-Eulerian Tours of Directed Graphs. - Matthew Farrell; Lionel Levine (Electron. J. Comb., 2016)

Dimensionality compression and expansion in Deep Neural Networks. - Stefano Recanatesi; Matthew Farrell; Madhu Advani; Timothy Moore; Guillaume Lajoie; Eric Shea-Brown (CoRR, 2019)

Autoencoder networks extract latent variables and encode these variables in their connectomes. - Matthew Farrell; Stefano Recanatesi; R. Clay Reid; Stefan Mihalas; Eric Shea-Brown (Neural Networks, 2021)

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? - Matthew Farrell; Blake Bordelon; Shubhendu Trivedi; Cengiz Pehlevan (ICLR, 2022)

Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. - Matthew Farrell; Stefano Recanatesi; Timothy Moore; Guillaume Lajoie; Eric Shea-Brown (Nat. Mach. Intell., 2022)

Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. - Matthew Farrell; Stefano Recanatesi; Timothy Moore; Guillaume Lajoie; Eric Shea-Brown (Nat. Mac. Intell., 2022)