Search Results for author: Megumi Sano

Found 4 papers, 2 papers with code

Active World Model Learning in Agent-rich Environments with Progress Curiosity

no code implementations ICML 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

World models are a family of predictive models that solve self-supervised problems on how the world evolves.

Visual resemblance and communicative context constrain the emergence of graphical conventions

1 code implementation17 Sep 2021 Robert D. Hawkins, Megumi Sano, Noah D. Goodman, Judith E. Fan

From photorealistic sketches to schematic diagrams, drawing provides a versatile medium for communicating about the visual world.

Active World Model Learning with Progress Curiosity

no code implementations15 Jul 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.

Cannot find the paper you are looking for? You can Submit a new open access paper.