2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2022
NashAE: Disentangling Representations through Adversarial Covariance Minimization
Eric Yeats, Frank Liu, David Womble +1
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no…
cs.LG2021★ 2 cited
On the Stochastic Stability of Deep Markov Models
Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang +2
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the f…
cs.LG2019
Single-Net Continual Learning with Progressive Segmented Training (PST)
Xiaocong Du, Gouranga Charan, Frank Liu +1
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learn…