Next, we develop algorithms for performing HSC (which we refer to as "inference rules"), and propose an efficient algorithm that guarantees a target accuracy

Automatically generating novel and interesting games is a complex task.

Environment Animals Wildlife Fish Self-piloting submarine set to begin historic mission to circle Earth's oceans Breakthroughs, discoveries, and DIY tips sent every weekday. An autonomous submersible named Redwing is heading out on a truly historic voyage. If successful, it will achieve the first around-the-world ocean trip made by an unpiloted underwater vehicle . Marine engineering company Teledyne Marine and researchers at Rutgers University in New Jersey are planning to launch the nearly nine-foot-long, specially outfitted Slocum Sentinel Glider on October 11 from Woods Hole Oceanographic Institution off the coast of Martha's Vineyard in Massachusetts. A livestream of the launch will be broadcast here, beginning at about 8:15 a.m. EDT on Saturday October 11.

With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.

In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function.

Image description datasets play a crucial role in the advancement of various applications such as image understanding, text-to-image generation, and text-image retrieval. Currently, image description datasets primarily originate from two sources. One source is the scraping of image-text pairs from the web. Despite their abundance, these descriptions are often of low quality and noisy.

Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale.

Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn more about the structure of the dataset than the computational structure of the network. There is therefore only indirect reason to believe that the directions found in these dictionaries are functionally important to the network. We propose end-to-end (e2e) sparse dictionary learning, a method for training SAEs that ensures the features learned are functionally important by minimizing the KL divergence between the output distributions of the original model and the model with SAE activations inserted.