Due to the cost and risk of deploying autonomous vehicles (A Vs) in the real world, simulation is a crucial tool in the research and development of autonomous driving software.

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale dataset with only image-levelannotations fordetector training.

Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.

We further evaluate SGBS+EAS on nine real-world based instance sets from [15]. Each instance set consists of 20 instances that have similar characteristics (i.e., they have been sampled from the same underlying distribution). To account for this new evaluation setting, we always perform 10 runs in parallel for EAS and SGBS+EAS. This improves the solution quality, while leading only to a slight increase of the requiredruntime. For SGBS+EAS we set (β, γ) = (35,5), the learning rate α = 0.005 and λ = 0.05.

Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches capable of high-quality solutions in a single shot are emerging, state-of-the-art approaches are often unable to take full advantage of the solving time available to them. In contrast, hand-crafted heuristics perform highly effective search well and exploit the computation time given to them, but contain heuristics that are difficult to adapt to a dataset being solved.

Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as apromising strategy for drug design.

We first demonstrate the capability of our system to learn a continuous latent texture space when trained on a dataset consisting of diverse textures (Section 1). In Section 5, we tabulate the network architectures of the convolutional neural networks used in our experiments. First and last square in each strip correspond to resynthesized exemplars. Note how our result is closer to the exemplar texture. See Figure 7 (second row) and Figure 8 (third row) for exemplar images.