Breakthroughs, discoveries, and DIY tips sent every weekday. Nature has once again proven to be an efficient designer, showing time and again how ant teamwork is much better than that of humans. "Teamwork is often assumed to enhance group performance, particularly for physical tasks. However, in both human and non-human animal teams, the effort contributed by each member may, in fact, decrease as team size grows," researchers wrote in a study recently published in the journal Current Biology. This phenomenon is called the Ringelmann effect.

A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to acquire concepts with the intended semantics.

Depending on the data distribution, the mechanisms can also discourage mis-reports that would lead to inaccurate predictions.

We develop a novel method for carrying out model selection for Bayesian autoen-coders (BAEs) by means of prior hyper-parameter optimization.

Recent works have parameterized the Hamiltonian by machine learning models (e.g., neural networks), allowing Hamiltonian dynamics to be

MILE is action-conditioned, allowing us to model how other agents respond to ego-actions. We show that our model can predict plausible and diverse futures from latent states and actions over long time horizons.

Multivariate time-series forecasting plays a crucial role in many real-world applications.