Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.

Molecular learning is pivotal in many real-world applications, such as drug discovery.

Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths. These days, rather than showing you the traditional list of links when you run a search query, Google is intent on throwing up AI Overviews instead: synthesized summaries of information scraped off the web, with some word-prediction magic added, and packaged together in a way to sound as accurate and reliable as possible. We've written before about some of the problems with these AI Overviews, which regularly contain mistakes or nonsense, and of course rip off the work of the human writers who actually know the answers to the questions you're putting into Google. There's another problem though--these AI answers can actually be dangerous. As with every other new technology through history, scams are now making their way into AI Overviews as well, apparently injecting Google's AI answers with fraudulent phone numbers that you shouldn't trust.

In addition, we introduce a simple baseline, "RDumb", that periodically resets the

Unmanned Aerial V ehicles (UA Vs), equipped with cameras, are employed in numerous applications, including aerial photography, surveillance, and agriculture. In these applications, robust object detection and tracking are essential for the effective deployment of UA Vs.

We present a method for text-driven perpetual view generation - synthesizing long-term videos of various scenes solely from an input text prompt describing the scene and camera poses.

Code is available at https://github.com/ZMHH-H/MoTE .

Existing point cloud based 3D detectors are designed for the particular scene, either indoor or outdoor ones.