Research to help build a better financial future
Our researchers undertake fundamental and applied research and use state-of-the-art ML to address some of the biggest challenges facing the financial services industry today and in the future.
Our widely published research covers a broad range of topics including Reinforcement learning, Natural Language Processing and Time Series Modeling. Our research is made freely available to support the AI community.
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Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2019
Authors:
*B. Kartal,
*P. Hernandez-Leal,
M. E. Taylor
* Denotes equal contribution
Action Guidance with MCTS for Deep Reinforcement Learning
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2019
Authors:
*B. Kartal,
*P. Hernandez-Leal,
M. E. Taylor
* Denotes equal contribution
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2019
Authors:
*B. Kartal,
*P. Hernandez-Leal,
M. E. Taylor
* Denotes equal contribution
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2019
Authors:
C. Gao,
B. Kartal,
P. Hernandez-Leal,
M. E. Taylor
On the Effectiveness of Low Frequency Perturbations
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
Y. Sharma,
G. W. Ding,
M. Brubaker
A Cross-Domain Transferable Neural Coherence Model
Association for Computational Linguistics (ACL), 2019
Authors:
P. Xu,
H. Saghir,
J. Kang,
L. Long,
A. J. Bose,
Y. Cao
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
The Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019
Authors:
C. Gao,
P. Hernandez-Leal,
B. Kartal,
M. E. Taylor
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
K. Young,
B. Wang,
M. E. Taylor
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Authors:
T. Durand,
N. Mehrasa,
G. Mori
A Variational Auto-Encoder Model for Stochastic Point Process
Conference on Computer Vision and Pattern Recognition (CVPR), 2019