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Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
A. Hajimoradlou, L. Pishdad, F. Tung, and M. Karpusha. Workshop at International Conference on Machine Learning (ICML), 2022
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Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
M. Amin Shabani, A. Abdi, L. Meng, and T. Sylvain. International Conference on Learning Representations (ICLR), 2023
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Agent Forecasting at Flexible Horizons using ODE Flows
Agent Forecasting at Flexible Horizons using ODE Flows
A. Radovic, J. He, J. Ramanan, M. Brubaker, and A. Lehrmann. International Conference on Machine Learning Workshop on Invertible Neural Nets and Normalizing Flows (ICML), 2021
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Continuous Latent Process Flows
Continuous Latent Process Flows
R. Deng, M. Brubaker, G. Mori, and A. Lehrmann. Conference on Neural Information Processing Systems (NeurIPS), 2021
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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
R. Deng, B. Chang, M. Brubaker, G. Mori, and A. Lehrmann. Conference on Neural Information Processing Systems (NeurIPS), 2020
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Point Process Flows
Point Process Flows
*N. Mehrasa, *R. Deng, M. O. Ahmed, B. Chang, J. He, T. Durand, M. Brubaker, and G. Mori. The Conference and Workshop on Neural Information Processing Systems Workshop on Learning with Temporal Point Processes (NeurIPS), 2019
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A Variational Auto-Encoder Model for Stochastic Point Process
A Variational Auto-Encoder Model for Stochastic Point Process
N. Mehrasa, A. Jyothi, T. Durand, J. He, L. Sigal, and G. Mori. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2019
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