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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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Neural Information Processing Systems (NeurIPS), 2020
Authors:
R. Deng,
B. Chang,
M. Brubaker
,
G. Mori
,
A. Lehrmann
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Conference on Neural Information Processing Systems (NeurIPS), 2020
Authors:
J. Yu,
K. Derpanis,
M. Brubaker
Generating Videos of Zero-Shot Compositions of Actions and Objects
European Conference on Computer Vision (ECCV), 2020
Authors:
M. Nawhal,
M. Zhai,
A. Lehrmann
,
L. Sigal
,
G. Mori
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Workshop on Invertible Neural Networks, Normalizing Flows and Explicit Likelihood Models(ICML), 2020
Authors:
R. Deng,
B. Chang,
M. Brubaker
,
G. Mori
,
A. Lehrmann
Evaluating Lossy Compression Rates of Deep Generative Models
International Conference on Machine Learning (ICML), 2020
Authors:
*S. Huang,
*A. Makhzani,
Y. Cao
,
R. Grosse
* Denotes equal contribution
Tails of Lipschitz Triangular Flows
International Conference on Machine Learning (ICML), 2020
Authors:
P. Jaini,
I. Kobyzev
,
Y. Yu,
M. Brubaker
Unsupervised Multilingual Alignment using Wasserstein Barycenter
International Joint Conference on Artificial Intelligence (IJCAI), 2020
Authors:
X. Lian,
K. Jain,
J. Truszkowski
,
P. Poupart,
Y. Yu
Learning User Representations for Open Vocabulary Image Hashtag Prediction
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Authors:
T. Durand
Normalizing Flows: An Introduction and Review of Current Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE), 2020
Authors:
I. Kobyzev
,
S. Prince
,
M. Brubaker