Automated Generation of Cognitive Ontology via Web Text-Mining. - Richard Gao; Thomas Donoghue; Bradley Voytek (CogSci, 2017)

Inferring synaptic excitation/inhibition balance from field potentials. - Richard Gao; Erik J. Peterson; Bradley Voytek (NeuroImage, 2017)

NeuroDSP: A package for neural digital signal processing. - Scott Cole; Thomas Donoghue; Richard Gao; Bradley Voytek (J. Open Source Softw., 2019)

For the Sciences They Are A-Changin': A Response to Commentaries on Núñez et al.'s (2019) "What Happened to Cognitive Science?". - Rafael E. Núñez; Michael Allen; Richard Gao; Carson Miller Rigoli; Josephine Relaford-Doyle; Arturs Semenuks (Top. Cogn. Sci., 2020)

Bridging cognition and neurobiology with large-scale cortical dynamics and multimodal brain data - Richard Gao (2020)

Systematic benchmarking of HTTPS third party copy on 100Gbps links using XRootD. - Edgar Fajardo Hernandez; Aashay Arora; Diego Davila; Richard Gao; Frank Würthwein; Brian Bockelman (CoRR, 2021)

SoK: Vehicle Orientation Representations for Deep Rotation Estimation. - Huahong Tu; Siyuan Peng; Vladimir Leung; Richard Gao (CoRR, 2021)

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. - Richard Gao; Michael Deistler; Jakob H. Macke (NeurIPS, 2023)

Simulation-based inference for efficient identification of generative models in computational connectomics. - Jan Boelts; Philipp Harth; Richard Gao; Daniel Udvary; Felipe Yáñez; Daniel Baum; Hans-Christian Hege; Marcel Oberländer; Jakob H. Macke (PLoS Comput. Biol., 2023)

GLDL: Graph Label Distribution Learning. - Yufei Jin; Richard Gao; Yi He 007; Xingquan Zhu 001 (AAAI, 2024)

Latent Diffusion for Neural Spiking Data. - Jaivardhan Kapoor; Auguste Schulz; Julius Vetter; Felix Pei; Richard Gao; Jakob H. Macke (NeurIPS, 2024)

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. - Julius Vetter; Guy Moss 001; Cornelius Schröder; Richard Gao; Jakob H. Macke (NeurIPS, 2024)

A Practical Guide to Statistical Distances for Evaluating Generative Models in Science. - Sebastian Bischoff 001; Alana Darcher; Michael Deistler; Richard Gao; Franziska Gerken; Manuel Glöckler; Lisa Haxel; Jaivardhan Kapoor; Janne K. Lappalainen; Jakob H. Macke; Guy Moss 001; Matthijs Pals; et al. (CoRR, 2024)

Neural timescales from a computational perspective. - Roxana Zeraati; Anna Levina; Jakob H. Macke; Richard Gao (CoRR, 2024)

Generating realistic neurophysiological time series with denoising diffusion probabilistic models. - Julius Vetter; Jakob H. Macke; Richard Gao (Patterns, 2024)

A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science. - Sebastian Bischoff 001; Alana Darcher; Michael Deistler; Richard Gao; Franziska Gerken; Manuel Glöckler; Lisa Haxel; Jaivardhan Kapoor; Janne K. Lappalainen; Jakob H. Macke; Guy Moss 001; Matthijs Pals; et al. (Trans. Mach. Learn. Res., 2024)

sbi reloaded: a toolkit for simulation-based inference workflows. - Jan Boelts; Michael Deistler; Manuel Glöckler; Álvaro Tejero-Cantero; Jan-Matthis Lueckmann; Guy Moss 001; Peter Steinbach; Thomas Moreau 001; Fabio Muratore; Julia Linhart; Conor Durkan; Julius Vetter; et al. (J. Open Source Softw., 2025)