Deep Learning has enabled remarkable progress over the last years on a variety of tasks,
such as image recognition, speech recognition, and machine translation. One crucial aspect …

Abstract Model-based Reinforcement Learning (MBRL) is a promising framework for
learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to …

Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which
intuitively imitates the learning style of human beings. It is commonly derived from solving …

Finding a well-performing architecture is often tedious for both deep learning practitioners
and researchers, leading to tremendous interest in the automation of this task by means of …

Abstract Background: Generative adversarial networks (GANs) are vital techniques for
synthesizing high-fidelity images. Recent studies have applied them to generation tasks …

Much research work in computer vision is being spent on optimizing existing network
architectures to obtain a few more percentage points on benchmarks. Recent AutoML …

Abstract Neural Architecture Search (NAS) has shown excellent results in designing
architectures for computer vision problems. NAS alleviates the need for human-defined …

A good state representation is crucial to solving complicated reinforcement learning (RL)
challenges. Many recent works focus on designing auxiliary losses for learning informative …

While Reinforcement Learning (RL) has made great strides towards solving increasingly
complicated problems, many algorithms are still brittle to even slight environmental changes …

Motivation RNA design shows growing applications in synthetic biology and therapeutics,
driven by the crucial role of RNA in various biological processes. A fundamental challenge is …