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20152023
most citedYour GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

49 citations · 275 across the 31 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.RO2020★ 1 cited

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi +10

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challen…

cs.LG2020★ 34 cited

La-MAML: Look-ahead Meta Learning for Continual Learning

Gunshi Gupta, Karmesh Yadav, Liam Paull

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning show…

cs.LG2020★ 49 cited

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…

cs.LG2020★ 8 cited

Curriculum in Gradient-Based Meta-Reinforcement Learning

Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy +2

Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcement learning settings. However, specific…

cs.LG2020★ 5 cited

Generating Automatic Curricula via Self-Supervised Active Domain Randomization

Sharath Chandra Raparthy, Bhairav Mehta, Florian Golemo +1

Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the complet…