Shuangshuang Chen — accepted AI-conference papers

2023

Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations

Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis, Mårten Björkman

ICLR 2023poster

Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased sampling. In this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster t…

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2021

Monte Carlo Filtering Objectives

Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis, Mårten Björkman

IJCAI 2021poster

Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a fa…

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