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2020 – today
- 2023
[i29]Isaac Tamblyn, Tengkai Yu, Ian Benlolo:
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems. CoRR abs/2301.01807 (2023)- [i28]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 abs/2305.14177 (2023) - [i27]Colin Bellinger, Mark Crowley, Isaac Tamblyn:
Learning when to observe: A frugal reinforcement learning framework for a high-cost world. CoRR abs/2307.02620 (2023) - 2022
- [j7]Sebastian Johann Wetzel
, Roger G. Melko, Isaac Tamblyn:
Twin neural network regression is a semi-supervised regression algorithm. Mach. Learn. Sci. Technol. 3(4): 45007 (2022) - [j6]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): 45026 (2022) - [c5]Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn:
Balancing Information with Observation Costs in Deep Reinforcement Learning. AI 2022 - [c4]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 - [i26]Corneel Casert, Isaac Tamblyn, Stephen Whitelam:
Learning stochastic dynamics and predicting emergent behavior using transformers. CoRR abs/2202.08708 (2022) - [i25]Stephen Whitelam, Isaac Tamblyn:
Cellular automata can classify data by inducing trajectory phase coexistence. CoRR abs/2203.05551 (2022) - [i24]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 abs/2204.02474 (2022) - [i23]Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn:
Machine Learning Diffusion Monte Carlo Energy Densities. CoRR abs/2205.04547 (2022) - [i22]Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn:
Training neural networks using Metropolis Monte Carlo and an adaptive variant. CoRR abs/2205.07408 (2022) - 2021
- [j5]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): 02 (2021) - [j4]Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik:
Scientific intuition inspired by machine learning-generated hypotheses. Mach. Learn. Sci. Technol. 2(2): 25027 (2021) - [c3]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Active Measure Reinforcement Learning for Observation Cost Minimization. Canadian Conference on AI 2021 - [i21]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 abs/2101.04383 (2021) - [i20]Kyle Mills, Isaac Tamblyn:
Weakly-supervised multi-class object localization using only object counts as labels. CoRR abs/2102.11743 (2021) - [i19]Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru-Guzik:
Golem: An algorithm for robust experiment and process optimization. CoRR abs/2103.03716 (2021) - [i18]Sebastian Johann Wetzel, Roger G. Melko, Isaac Tamblyn:
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm. CoRR abs/2106.06124 (2021) - [i17]Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn:
Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. CoRR abs/2112.07535 (2021) - [i16]Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, Isaac Tamblyn:
Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment. CoRR abs/2112.14657 (2021) - 2020
- [j3]Kyle Mills, Pooya Ronagh, Isaac Tamblyn:
Finding the ground state of spin Hamiltonians with reinforcement learning. Nat. Mach. Intell. 2(9): 509-517 (2020) - [c2]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. Canadian Conference on AI 2020: 55-66 - [i15]Kyle Mills, Pooya Ronagh, Isaac Tamblyn:
Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning. CoRR abs/2003.00011 (2020) - [i14]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 abs/2003.02647 (2020) - [i13]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. CoRR abs/2004.07333 (2020) - [i12]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Active Measure Reinforcement Learning for Observation Cost Minimization. CoRR abs/2005.12697 (2020) - [i11]Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn:
Correspondence between neuroevolution and gradient descent. CoRR abs/2008.06643 (2020) - [i10]Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik:
Scientific intuition inspired by machine learning generated hypotheses. CoRR abs/2010.14236 (2020) - [i9]Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn:
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. CoRR abs/2011.08657 (2020) - [i8]M. Lytova, Michael Spanner, Isaac Tamblyn:
Deep learning and high harmonic generation. CoRR abs/2012.10328 (2020) - [i7]Stephen Whitelam, Isaac Tamblyn:
Neuroevolutionary learning of particles and protocols for self-assembly. CoRR abs/2012.11832 (2020) - [i6]Sebastian Johann Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn:
Twin Neural Network Regression. CoRR abs/2012.14873 (2020)
2010 – 2019
- 2019
- [i5]Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn:
Optimizing thermodynamic trajectories using evolutionary reinforcement learning. CoRR abs/1903.08543 (2019) - [i4]Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn:
Evolutionary reinforcement learning of dynamical large deviations. CoRR abs/1909.00835 (2019) - [i3]Stephen Whitelam, Isaac Tamblyn:
Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. CoRR abs/1912.08333 (2019) - 2017
- [j2]Nataliya Portman, Isaac Tamblyn:
Sampling algorithms for validation of supervised learning models for Ising-like systems. J. Comput. Phys. 350: 871-890 (2017) - [j1]Kevin Ryczko, Adam Domurad, Nicholas Buhagiar, Isaac Tamblyn:
Hashkat: large-scale simulations of online social networks. Soc. Netw. Anal. Min. 7(1): 4:1-4:13 (2017) - [i2]Kyle Mills, Michael Spanner, Isaac Tamblyn:
Deep learning and the Schrödinger equation. CoRR abs/1702.01361 (2017) - 2016
- [i1]Kevin Ryczko, Adam Domurad, Nicholas Buhagiar, Isaac Tamblyn:
Hashkat: Large-scale simulations of online social networks. CoRR abs/1610.07458 (2016)
2000 – 2009
- 2008
- [c1]Isaac Tamblyn, Stanimir A. Bonev:
Exploring the High Pressure Phase Diagrams of Light Elements Using Large Scale Ab-initio Molecular Dynamics Simulations. HPCS 2008: 154-160
Coauthor Index
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last updated on 2023-09-08 12:14 CEST by the dblp team
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