One approach toward automatic grading is to learn an agent that interacts with a student's program and explores states indicative of errors via reinforcement learning. However, existing work on this approach only provides binary feedback of whether a program is correct or not, while students require finer-grained feedback on the specific errors in their programs to understand their mistakes. In this work, we show that exploring to discover errors can be cast as a meta-exploration problem.

Self-supervised pre-training recently demonstrates success on large-scale multi-modal data, and state-of-the-art contrastive learning methods often enforce the feature consistency from cross-modality inputs, such as video/audio or video/text pairs.

While deep learning's performance has led it to become the dominant paradigm in machine learning,

A plethora of aspects on the robustness have been studied, ranging from algorithms to their initialization as well as from the width of neural networks to their depth (i.e., the architecture).

OpenAI is reportedly on the verge of a roughly 500 billion valuation, a figure that would make it the most valuable private company in the world--bigger than SpaceX, TikTok's parent company Bytedance, and even public giants like Palantir. It's a staggering number for a company with an "astronomical burn rate." How is this even possible? As Axios reports, there are actually two deals in play: a SoftBank-led round valuing the company at 300 billion, which won't close until year's end, and a secondary sale of employee shares at a far steeper 500 billion valuation. Most of the cheaper shares have already been snapped up, leaving investors to fight over the pricier ones.