Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable
materials and usually follow a bioinspired design. Their high dexterity and safety make them …

The power of human language and thought arises from systematic compositionality—the
algebraic ability to understand and produce novel combinations from known components …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Large transformer-based models are able to perform in-context few-shot learning, without
being explicitly trained for it. This observation raises the question: what aspects of the …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Structured state space sequence (S4) models have recently achieved state-of-the-art
performance on long-range sequence modeling tasks. These models also have fast …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

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 …