Isaac Tamblyn

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 41

Top Topics

Cellular Automata

Large Deviations

Reinforcement Learning

Top Venues

Mach. Learn. Sci. Technol.

Canadian Conference on AI

Publications

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
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. CoRR (2023)
Isaac Tamblyn, Tengkai Yu, Ian Benlolo
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems. CoRR (2023)
Vincent Létourneau, Colin Bellinger, Isaac Tamblyn, Maia Fraser
Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting. CoLLAs (2023)
Colin Bellinger, Mark Crowley, Isaac Tamblyn
Learning when to observe: A frugal reinforcement learning framework for a high-cost world. CoRR (2023)
Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn
Training neural networks using Metropolis Monte Carlo and an adaptive variant. Mach. Learn. Sci. Technol. 3 (4) (2022)
Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn
Training neural networks using Metropolis Monte Carlo and an adaptive variant. CoRR (2022)
Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn
Balancing Information with Observation Costs in Deep Reinforcement Learning. AI (2022)
Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn
Machine Learning Diffusion Monte Carlo Energy Densities. CoRR (2022)
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, Hsu Kiang Ooi
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge. CoRR (2022)
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, Hsu Kiang Ooi
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge. AI (2022)
Corneel Casert, Isaac Tamblyn, Stephen Whitelam
Learning stochastic dynamics and predicting emergent behavior using transformers. CoRR (2022)
Sebastian Johann Wetzel, Roger G. Melko, Isaac Tamblyn
Twin neural network regression is a semi-supervised regression algorithm. Mach. Learn. Sci. Technol. 3 (4) (2022)
Stephen Whitelam, Isaac Tamblyn
Cellular automata can classify data by inducing trajectory phase coexistence. CoRR (2022)
Kyle Mills, Isaac Tamblyn
Weakly-supervised multi-class object localization using only object counts as labels. CoRR (2021)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Active Measure Reinforcement Learning for Observation Cost Minimization. Canadian Conference on AI (2021)
Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn
Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. CoRR (2021)
Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru-Guzik
Golem: An algorithm for robust experiment and process optimization. CoRR (2021)
Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik
Scientific intuition inspired by machine learning-generated hypotheses. Mach. Learn. Sci. Technol. 2 (2) (2021)
Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. Mach. Learn. Sci. Technol. 2 (2) (2021)
Sebastian Johann Wetzel, Roger G. Melko, Isaac Tamblyn
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm. CoRR (2021)
Hitarth Choubisa, Petar Todorovic, Joao M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy, Isaac Tamblyn, Edward H. Sargent
Interpretable discovery of new semiconductors with machine learning. CoRR (2021)
Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, Isaac Tamblyn
Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment. CoRR (2021)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. Canadian Conference on AI (2020)
Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. CoRR (2020)
Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn
Correspondence between neuroevolution and gradient descent. CoRR (2020)
Sebastian Johann Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn
Twin Neural Network Regression. CoRR (2020)
M. Lytova, Michael Spanner, Isaac Tamblyn
Deep learning and high harmonic generation. CoRR (2020)
Kyle Mills, Pooya Ronagh, Isaac Tamblyn
Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning. CoRR (2020)
Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. CoRR (2020)
Stephen Whitelam, Isaac Tamblyn
Neuroevolutionary learning of particles and protocols for self-assembly. CoRR (2020)
Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik
Scientific intuition inspired by machine learning generated hypotheses. CoRR (2020)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Active Measure Reinforcement Learning for Observation Cost Minimization. CoRR (2020)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. CoRR (2020)
Kyle Mills, Pooya Ronagh, Isaac Tamblyn
Finding the ground state of spin Hamiltonians with reinforcement learning. Nat. Mach. Intell. 2 (9) (2020)
Stephen Whitelam, Isaac Tamblyn
Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. CoRR (2019)
Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
Evolutionary reinforcement learning of dynamical large deviations. CoRR (2019)
Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn
Optimizing thermodynamic trajectories using evolutionary reinforcement learning. CoRR (2019)
Kevin Ryczko, Adam Domurad, Nicholas Buhagiar, Isaac Tamblyn
Hashkat: large-scale simulations of online social networks. Soc. Netw. Anal. Min. 7 (1) (2017)
Nataliya Portman, Isaac Tamblyn
Sampling algorithms for validation of supervised learning models for Ising-like systems. J. Comput. Phys. 350 (2017)
Kyle Mills, Michael Spanner, Isaac Tamblyn
Deep learning and the Schrödinger equation. CoRR (2017)
Kevin Ryczko, Adam Domurad, Nicholas Buhagiar, Isaac Tamblyn
Hashkat: Large-scale simulations of online social networks. CoRR (2016)
Isaac Tamblyn, Stanimir A. Bonev
Exploring the High Pressure Phase Diagrams of Light Elements Using Large Scale Ab-initio Molecular Dynamics Simulations. HPCS (2008)