Isaac Tamblyn

According to our database, Isaac Tamblyn authored at least 35 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Learning when to observe: A frugal reinforcement learning framework for a high-cost world.

Colin Bellinger, Mark Crowley, Isaac Tamblyn

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, Isaac Tamblyn

CoRR, 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

Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting.

Vincent Létourneau, Colin Bellinger, Isaac Tamblyn, Maia Fraser

Proceedings of the Conference on Lifelong Learning Agents, 2023

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., December, 2022

Twin neural network regression is a semi-supervised regression algorithm.

Sebastian Johann Wetzel, Roger G. Melko, Isaac Tamblyn

Mach. Learn. Sci. Technol., December, 2022

Training neural networks using Metropolis Monte Carlo and an adaptive variant.

Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn

CoRR, 2022

Machine Learning Diffusion Monte Carlo Energy Densities.

Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn

CoRR, 2022

Cellular automata can classify data by inducing trajectory phase coexistence.

Stephen Whitelam, Isaac Tamblyn

CoRR, 2022

Learning stochastic dynamics and predicting emergent behavior using transformers.

Corneel Casert, Isaac Tamblyn, Stephen Whitelam

CoRR, 2022

Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge.

Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, Hsu Kiang Ooi

Proceedings of the 35th Canadian Conference on Artificial Intelligence, Toronto, Ontario, 2022

Balancing Information with Observation Costs in Deep Reinforcement Learning.

Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn

Proceedings of the 35th Canadian Conference on Artificial Intelligence, Toronto, Ontario, 2022

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

Scientific intuition inspired by machine learning-generated hypotheses.

Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment.

Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, Isaac Tamblyn

CoRR, 2021

Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning.

Colin Bellinger, Andriy Drozdyuk, Mark Crowley, 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

Weakly-supervised multi-class object localization using only object counts as labels.

Kyle Mills, Isaac Tamblyn

CoRR, 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

Active Measure Reinforcement Learning for Observation Cost Minimization.

Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Proceedings of the 34th Canadian Conference on Artificial Intelligence, 2021

2020

Finding the ground state of spin Hamiltonians with reinforcement learning.

Kyle Mills, Pooya Ronagh, Isaac Tamblyn

Nat. Mach. Intell., 2020

Twin Neural Network Regression.

Sebastian Johann Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn

CoRR, 2020

Neuroevolutionary learning of particles and protocols for self-assembly.

Stephen Whitelam, Isaac Tamblyn

CoRR, 2020

Deep learning and high harmonic generation.

M. Lytova, Michael Spanner, 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

Correspondence between neuroevolution and gradient descent.

Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn

CoRR, 2020

Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning.

Kyle Mills, Pooya Ronagh, Isaac Tamblyn

CoRR, 2020

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment.

Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Proceedings of the Advances in Artificial Intelligence, 2020

2019

Learning to grow: control of materials self-assembly using evolutionary reinforcement learning.

Stephen Whitelam, Isaac Tamblyn

CoRR, 2019

Evolutionary reinforcement learning of dynamical large deviations.

Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn

CoRR, 2019

Optimizing thermodynamic trajectories using evolutionary reinforcement learning.

Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn

CoRR, 2019

2017

Hashkat: large-scale simulations of online social networks.

Kevin Ryczko, Adam Domurad, Nicholas Buhagiar, Isaac Tamblyn

Soc. Netw. Anal. Min., 2017

Sampling algorithms for validation of supervised learning models for Ising-like systems.

Nataliya Portman, Isaac Tamblyn

J. Comput. Phys., 2017

Deep learning and the Schrödinger equation.

Kyle Mills, Michael Spanner, Isaac Tamblyn

CoRR, 2017

2008

Exploring the High Pressure Phase Diagrams of Light Elements Using Large Scale Ab-initio Molecular Dynamics Simulations.

Isaac Tamblyn, Stanimir A. Bonev

Proceedings of the 22nd Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2008), 2008