Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Deep neural networks, and in particular recurrent networks, are promising candidates to control autonomous agents that interact in real-time with the physical world. However, this requires a seamless integration of temporal features into the network's architecture. For the training of and inference with recurrent neural networks, they are usually rolled out over time, and different rollouts exist.

For decades, handmade narco subs have been some of the cocaine trade's most elusive and productive workhorses, ferrying multi-ton loads of illicit drugs from Colombian estuaries toward markets in North America and, increasingly, the rest of the world. Now off-the-shelf technology--Starlink terminals, plug-and-play nautical autopilots, high-resolution video cameras--may be advancing that cat-and-mouse game into a new phase. Uncrewed subs could move more cocaine over longer distances, and they wouldn't put human smugglers at risk of capture. And law enforcement around the world is just beginning to grapple with what this means for the future. This story is from the next print issue of magazine, which is all about crime. Google DeepMind is calling for the moral behavior of large language models--such as what they do when called on to act as companions, therapists, medical advisors, and so on--to be scrutinized with the same kind of rigor as their ability to code or do math.

Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities.

Time series with non-uniform intervals occur in many applications, and are difficulttomodel usingstandard recurrent neural networks(RNNs).

While these advances use different algorithmic techniques, e.g., contrastive learning, diffusion, or auto-regressive modeling, they all rest on a common foundation: large datasets containing paired image-text examples.