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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Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
European Chapter of the Association for Computational Linguistics (EACL), 2021
Authors: V. Balasubramanian, I. Kobyzev, H. Bahuleyan, O. Vechtomova
Abstract Paper BibTeX
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
European Chapter of the Association for Computational Linguistics (EACL), 2021
Authors: C. Emmery, A. Kádár, G. Chrupała
Abstract Paper BibTeX
Learning Discriminative Prototypes with Dynamic Time Warping
Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Authors: X. Chang , F. Tung , G. Mori
Abstract Paper BibTeX
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Authors: Y. Gong, H. Hajimirsadeghi , J. He , T. Durand , G. Mori
Abstract Paper BibTeX
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Conference on Neural Information Processing Systems (NeurIPS), 2020
Authors: J. Yu, K. Derpanis, M. Brubaker
Abstract Paper BibTeX Blog Code
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
Abstract Paper BibTeX Blog
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
Abstract Paper BibTeX
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
Abstract Paper BibTeX
On Variational Learning of Controllable Representations for Text without Supervision
International Conference on Machine Learning (ICML), 2020
Authors: P. Xu , Y. Cao
Abstract Paper BibTeX
Tails of Lipschitz Triangular Flows
International Conference on Machine Learning (ICML), 2020
Authors: P. Jaini, I. Kobyzev , Y. Yu, M. Brubaker
Abstract Paper BibTeX Code
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
Abstract Paper BibTeX