229 citations · 774 across the 8 of their papers we have counts for
8 papers
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang Zhang, Tong Che, Boris Ivanovic +4
Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a…
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein +4
We show that the sum of the implicit generator log-density of a GAN with the logit score of the discriminator defines an energy function which yields the true data densi…
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che, Xiaofeng Liu, Site Li +4
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably ver…
Residual Connections Encourage Iterative Inference
Stanisław Jastrzębski, Devansh Arpit, Nicolas Ballas +3
Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent…
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che, Yanran Li, Ruixiang Zhang +4
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather…
Boundary-Seeking Generative Adversarial Networks
R Devon Hjelm, Athul Paul Jacob, Tong Che +3
Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributio…