Jonathon Shlens

TL;DR: In this paper, the authors developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals, inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience.

TL;DR: In this article, a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents is proposed, inspired by recent language modeling approaches, enabling one to invoke a single model to predict agent behavior in many ways, such as potentially conditioned on the goal or full future trajectory of the autonomous vehicle or the behaviour of other agents.

TL;DR: In this article, the authors probed neural code variation using ~100 neural population recordings from major ganglion cell types in the macaque retina, combined with an interpretable computational representation of individual variability using machine learning.

TL;DR: In this article, an option may have an option score associated with an associated relation score, and a relation score may be calculated for a first option and a second option corresponding to a second object in an image.

TL;DR: In this paper, the drawing process of fonts is modeled by building sequential generative models of vector graphics, which have the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation.