128 citations · 207 across the 32 of their papers we have counts for
5 papers · 1 filter
Point Process Flows
Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed +5
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly model…
Lifelong GAN: Continual Learning for Conditional Image Generation
Mengyao Zhai, Lei Chen, Fred Tung +3
Lifelong learning is challenging for deep neural networks due to their susceptibility to catastrophic forgetting. Catastrophic forgetting occurs when a trained network is not able…
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
Akash Abdu Jyothi, Thibaut Durand, Jiawei He +2
Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignor…
Generative Model with Dynamic Linear Flow
Huadong Liao, Jiawei He, Kunxian Shu
Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modeling complex d…
A Variational Auto-Encoder Model for Stochastic Point Processes
Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand +3
We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the…