In real-world decision-making tasks, learning an optimal policy without a trialand-error process is an appealing challenge. When expert demonstrations are available, imitation learning that mimics expert actions can learn a good policy efficiently.

Despite strong empirical performance for image classification, deep neural networks are often regarded as"black boxes" and theyare difficult tointerpret.

Reconstructing a continuous surface from a raw 3D point cloud is a challenging task.

Intheexamplein Figure 2, the queryqi is "Loadalltablesfromdataset". Conferenceon Reverse Engineering (WCRE 2002), 28 October - 1 November 2002, VA, USAIEEE Computer Society, 2002.

Semi-supervised learning aims to leverage alarge amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. Inthispaper,wedelveintosemi-supervised learning forobject detection, where labeled data are more labor-intensive to collect.

Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predictive distributions, exact inference is not tractable. Approximate inference techniques trade off the ability to closely resemble the posterior distribution against speed of convergence and computational efficiency. We propose a novel Gaussian variational family that allows for retaining covariances between latent processes while achieving fast convergence by marginalising out all global latent variables. After providing a proof of how this marginalisation can be done for general covariances, we restrict them to the ones we empirically found to be most important in order to also achieve computational efficiency. We provide an efficient implementation of our new approach and apply it to several benchmark datasets. It yields excellent results and strikes a better balance between accuracy and calibrated uncertainty estimates than its state-of-the-art alternatives.

For a given set of points, define a value called "weight" for each point in (a),

Absolute relative error (ARE) achieved by adversarial perturbations of differentnorms(ξ)fordifferentscales.

We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to selectively alter the perceived geometry of the scene.