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 resesarch is made freely available to support the AI community.
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Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Workshop on Reinforcement Learning in Games (AAAI), 2019
Authors:
B. Kartal,
P. Hernandez-Leal,
M. E. Taylor
Compositional Hard Negatives for Visual Semantic Embeddings via an Adversary
Workshop on ViGIL (NeurIPS), 2018
Authors:
*A. J. Bose,
*H. Ling,
Y. Cao
* Denotes equal contribution
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
Neural Information Processing Systems (NeurIPS), 2018
Authors:
K. Lui,
G. W. Ding,
R. Huang,
R.J. McCann
On Learning Wire-Length Efficient Neural Networks
Workshop on Compact Deep Neural Network Representation (NeurIPS), 2018
Authors:
L. Wang,
G. Castiglione,
C. Srinivasa,
M. Brubaker
Skill Reuse in Partially Observable Multiagent Environments
Workshop on Latinx in AI Coalition (NeurIPS), 2018
Authors:
P. Hernandez-Leal,
B. Kartal,
M. E. Taylor
Adversarial Contrastive Estimation
Association for Computational Linguistics (ACL), 2018
Authors:
*A. J. Bose,
*H. Ling,
*Y. Cao
* Denotes equal contribution
Improving Reinforcement Learning with Human Input
International Joint Conference on Artificial Intelligence (IJCAI), 2018
Authors:
M. E. Taylor
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
International Conference on Learning Representations (ICLR), 2018
Authors:
Y. Cao,
G. W. Ding,
K. Lui,
R. Huang
Min-Max Propagation
Neural Information Processing Systems (NIPS), 2017
Authors:
C. Srinivasa,
I. Givoni,
S. Ravanbahksh,
B. J. Frey