Validation and Calibration of Human Performance Models to Support Simulation-Based Acquisition. - Floyd Glenn; Kelly Neville; James Stokes; Joan Ryder (WSC, 2004)

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. - Tengyuan Liang; Tomaso A. Poggio; Alexander Rakhlin; James Stokes (AISTATS, 2019)

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. - Tengyuan Liang; James Stokes (AISTATS, 2019)

Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks. - Seungwon Lee; James Stokes; Eric Eaton (IJCAI, 2019)

Mean-field Analysis of Batch Normalization. - Mingwei Wei; James Stokes; David J. Schwab (CoRR, 2019)

Probabilistic Modeling with Matrix Product States. - James Stokes; John Terilla (Entropy, 2019)

Natural evolution strategies and quantum approximate optimization. - Tianchen Zhao; Giuseppe Carleo; James Stokes; Shravan K. Veerapaneni (CoRR, 2020)

Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states. - James Stokes; Javier Robledo Moreno; Eftychios A. Pnevmatikakis; Giuseppe Carleo (CoRR, 2020)

Meta Variational Monte Carlo. - Tianchen Zhao; James Stokes; Oliver Knitter; Brian Chen; Shravan K. Veerapaneni (CoRR, 2020)

Gauge equivariant neural networks for quantum lattice gauge theories. - Di Luo; Giuseppe Carleo; Bryan K. Clark; James Stokes (CoRR, 2020)

Quantum Natural Gradient. - James Stokes; Josh A. Izaac; Nathan Killoran; Giuseppe Carleo (Quantum, 2020)

Overcoming barriers to scalability in variational quantum Monte Carlo. - Tianchen Zhao; Saibal De; Brian Chen; James Stokes; Shravan K. Veerapaneni (SC, 2021)

Natural evolution strategies and variational Monte Carlo. - Tianchen Zhao; Giuseppe Carleo; James Stokes; Shravan K. Veerapaneni (Mach. Learn. Sci. Technol., 2021)

Numerical and geometrical aspects of flow-based variational quantum Monte Carlo. - James Stokes; Brian Chen; Shravan K. Veerapaneni (CoRR, 2022)

Quantum-inspired variational algorithms for partial differential equations: Application to financial derivative pricing. - Tianchen Zhao; Chuhao Sun; Asaf Cohen 003; James Stokes; Shravan K. Veerapaneni (CoRR, 2022)

Scalable neural quantum states architecture for quantum chemistry. - Tianchen Zhao; James Stokes; Shravan K. Veerapaneni (CoRR, 2022)

Toward Neural Network Simulation of Variational Quantum Algorithms. - Oliver Knitter; James Stokes; Shravan K. Veerapaneni (CoRR, 2022)

Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation. - Di Luo; Shunyue Yuan; James Stokes; Bryan K. Clark (CoRR, 2022)

Digital quantum simulation of Schrödinger dynamics using adaptive approximations of potential functions. - Tenzan Araki; James Stokes; Shravan K. Veerapaneni (CoRR, 2022)

Quantum-inspired nonlinear Galerkin ansatz for high-dimensional HJB equations. - Chuhao Sun; Asaf Cohen 003; James Stokes; Shravan K. Veerapaneni (CoRR, 2023)

Continuous-variable neural network quantum states and the quantum rotor model. - James Stokes; Saibal De; Shravan K. Veerapaneni; Giuseppe Carleo (Quantum Mach. Intell., 2023)

Meta-variational quantum Monte Carlo. - Tianchen Zhao; James Stokes; Shravan K. Veerapaneni (Quantum Mach. Intell., 2023)

Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry. - Oliver Knitter; Dan Zhao; James Stokes; Martin Ganahl; Stefan Leichenauer; Shravan K. Veerapaneni (CoRR, 2024)

Retentive neural quantum states: efficient ansätze for ab initio quantum chemistry. - Oliver Knitter; Dan Zhao; James Stokes; Martin Ganahl; Stefan Leichenauer; Shravan K. Veerapaneni (Mach. Learn. Sci. Technol., 2025)

Variational quantum and neural quantum states algorithms for the linear complementarity problem. - Saibal De; Oliver Knitter; Rohan Kodati; Paramsothy Jayakumar; James Stokes; Shravan K. Veerapaneni (CoRR, 2025)