Exploring the High Pressure Phase Diagrams of Light Elements Using Large Scale Ab-initio Molecular Dynamics Simulations. - Isaac Tamblyn; Stanimir A. Bonev (HPCS, 2008)

Deep learning and the Schrödinger equation. - Kyle Mills; Michael Spanner; Isaac Tamblyn (CoRR, 2017)

Sampling algorithms for validation of supervised learning models for Ising-like systems. - Nataliya Portman; Isaac Tamblyn (J. Comput. Phys., 2017)

Hashkat: large-scale simulations of online social networks. - Kevin Ryczko; Adam Domurad; Nicholas Buhagiar; Isaac Tamblyn (Soc. Netw. Anal. Min., 2017)

Optimizing thermodynamic trajectories using evolutionary reinforcement learning. - Chris Beeler; Uladzimir Yahorau; Rory Coles; Kyle Mills; Stephen Whitelam; Isaac Tamblyn (CoRR, 2019)

Evolutionary reinforcement learning of dynamical large deviations. - Stephen Whitelam; Daniel Jacobson; Isaac Tamblyn (CoRR, 2019)

Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2019)

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. - Colin Bellinger; Rory Coles; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 2020)

Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning. - Kyle Mills; Pooya Ronagh; Isaac Tamblyn (CoRR, 2020)

Correspondence between neuroevolution and gradient descent. - Stephen Whitelam; Viktor Selin; Sang-Won Park; Isaac Tamblyn (CoRR, 2020)

Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. - Corneel Casert; Tom Vieijra; Stephen Whitelam; Isaac Tamblyn (CoRR, 2020)

Deep learning and high harmonic generation. - M. Lytova; Michael Spanner; Isaac Tamblyn (CoRR, 2020)

Neuroevolutionary learning of particles and protocols for self-assembly. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2020)

Twin Neural Network Regression. - Sebastian Johann Wetzel; Kevin Ryczko; Roger G. Melko; Isaac Tamblyn (CoRR, 2020)

Finding the ground state of spin Hamiltonians with reinforcement learning. - Kyle Mills; Pooya Ronagh; Isaac Tamblyn (Nat. Mach. Intell., 2020)

Active Measure Reinforcement Learning for Observation Cost Minimization. - Colin Bellinger; Rory Coles; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 2021)

Interpretable discovery of new semiconductors with machine learning. - Hitarth Choubisa; Petar Todorovic; Joao M. Pina; Darshan H. Parmar; Ziliang Li; Oleksandr Voznyy; Isaac Tamblyn; Edward H. Sargent (CoRR, 2021)

Weakly-supervised multi-class object localization using only object counts as labels. - Kyle Mills; Isaac Tamblyn (CoRR, 2021)

Golem: An algorithm for robust experiment and process optimization. - Matteo Aldeghi; Florian Häse; Riley J. Hickman; Isaac Tamblyn; Alán Aspuru-Guzik (CoRR, 2021)

Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. - Colin Bellinger; Andriy Drozdyuk; Mark Crowley 001; Isaac Tamblyn (CoRR, 2021)

Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment. - Chris Beeler; Xinkai Li; Mark Crowley 001; Maia Fraser; Isaac Tamblyn (CoRR, 2021)

Scientific intuition inspired by machine learning-generated hypotheses. - Pascal Friederich; Mario Krenn; Isaac Tamblyn; Alán Aspuru-Guzik (Mach. Learn. Sci. Technol., 2021)

Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. - Kyle Sprague; Juan Carrasquilla; Stephen Whitelam; Isaac Tamblyn (Mach. Learn. Sci. Technol., 2021)

Balancing Information with Observation Costs in Deep Reinforcement Learning. - Colin Bellinger; Andriy Drozdyuk; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 2022)

Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge. - Mohammad Sajjad Ghaemi; Karl Grantham; Isaac Tamblyn; Yifeng Li 001; Hsu Kiang Ooi (Canadian AI, 2022)

Learning stochastic dynamics and predicting emergent behavior using transformers. - Corneel Casert; Isaac Tamblyn; Stephen Whitelam (CoRR, 2022)

Cellular automata can classify data by inducing trajectory phase coexistence. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2022)

Machine Learning Diffusion Monte Carlo Energy Densities. - Kevin Ryczko; Jaron T. Krogel; Isaac Tamblyn (CoRR, 2022)

Training neural networks using Metropolis Monte Carlo and an adaptive variant. - Stephen Whitelam; Viktor Selin; Ian Benlolo; Isaac Tamblyn (CoRR, 2022)

Twin neural network regression is a semi-supervised regression algorithm. - Sebastian Johann Wetzel; Roger G. Melko; Isaac Tamblyn (Mach. Learn. Sci. Technol., 2022)

Training neural networks using Metropolis Monte Carlo and an adaptive variant. - Stephen Whitelam; Viktor Selin; Ian Benlolo; Corneel Casert; Isaac Tamblyn (Mach. Learn. Sci. Technol., 2022)

Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting. - Vincent Létourneau; Colin Bellinger; Isaac Tamblyn; Maia Fraser (CoLLAs, 2023)

Learning When to Observe: A Frugal Reinforcement Learning Framework for a High-Cost World. - Colin Bellinger; Isaac Tamblyn; Mark Crowley 001 (PKDD/ECML Workshops (4), 2023)

fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems. - Isaac Tamblyn; Tengkai Yu; Ian Benlolo (CoRR, 2023)

ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. - Chris Beeler; Sriram Ganapathi Subramanian; Kyle Sprague; Nouha Chatti; Colin Bellinger; Mitchell Shahen; Nicholas Paquin; Mark Baula; Amanuel Dawit; Zihan Yang; Xinkai Li; Mark Crowley 001; et al. (CoRR, 2023)

Dynamic programming with incomplete information to overcome navigational uncertainty in POMDPs. - Chris Beeler; Xinkai Li; Colin Bellinger; Mark Crowley 001; Maia Fraser; Isaac Tamblyn (Canadian AI, 2024)