Practical estimation of probability densities using scale-free field theories. - Justin B. Kinney (CoRR, 2013)

Unification of Field Theory and Maximum Entropy Methods for Learning Probability Densities. - Justin B. Kinney (CoRR, 2014)

Parametric Inference in the Large Data Limit Using Maximally Informative Models. - Justin B. Kinney; Gurinder S. Atwal (Neural Comput., 2014)

Density estimation on small datasets. - Wei-Chia Chen; Ammar Tareen; Justin B. Kinney (CoRR, 2018)

Mapping DNA sequence to transcription factor binding energy in vivo. - Stephanie L. Barnes; Nathan M. Belliveau; William T. Ireland; Justin B. Kinney; Rob Phillips (PLoS Comput. Biol., 2019)

Logomaker: beautiful sequence logos in Python. - Ammar Tareen; Justin B. Kinney (Bioinform., 2020)

Biophysical models of cis-regulation as interpretable neural networks. - Ammar Tareen; Justin B. Kinney (CoRR, 2020)

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models. - Evan Seitz; David M. McCandlish; Justin B. Kinney; Peter K. Koo (Nat. Mac. Intell., 2024)

Gauge fixing for sequence-function relationships. - Anna Posfai; Juannan Zhou; David M. McCandlish; Justin B. Kinney (PLoS Comput. Biol., 2025)

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing. - Samantha Petti; Carlos Martí-Gómez; Justin B. Kinney; Juannan Zhou; David M. McCandlish (CoRR, 2025)