Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

Recent advances in robot learning have shown promise in enabling robots to perform a
variety of manipulation tasks and generalize to novel scenarios. One of the key contributing …

Can a user create a deep generative model by sketching a single example? Traditionally,
creating a GAN model has required the collection of a large-scale dataset of exemplars and …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing only
previously collected experience, without any online interaction. While it is widely understood …

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation policies and …