The design of a reward function often poses a major practical challenge to realworld applications of reinforcement learning.

"is it useful to also learn alpha and beta in eqn 4?", "Does it17 suffice to just learn g and set the forcing term to 0?": We have finished all these three set of experiments. Unlikeourend-to-end architecture, mostpriorworks35 use either a single DMP to represent the whole trajectory or the trajectory is manually segmented to learn different36 DMPs.

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

I am inclined to increase my score if the weaknesses are fixed

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design.

Few objects pique the curiosity of academics quite like the Herculaneum scrolls – the ancient documents buried by the eruption of Mount Vesuvius in AD 79. These priceless rolled-up documents carry ancient text written in carbon-based ink on papyrus – a material similar to paper made from the pith of the papyrus plant. Now, for the first time, researchers have found the title and author inside one of the scrolls known as PHerc. With the help of AI, it has been identified as'On Vices' by the Greek philosopher Philodemus, a historic ethical treatise providing guidance for'cultivating a virtuous life'. In recognition of this achievement, researchers have been awarded the Vesuvius Challenge First Title Prize, which includes prize money of 60,000/ 45,000.