Recent progress in computer vision has been driven by a simple yet transformative
observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...
Alumni studies are often overlooked in engineering education research, despite holding great potential for improving engineering programmes and creating the links that are missed when it comes to university-workplace transitions. Besides better understandi ...
We study the settings where we are given a separable objective function of n variables defined in a given box of integers. We show that in many cases we can replace the given objective function by a new function with a much smaller domain. Our results appl ...
Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...
