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Found 4 papers, 1 papers with code
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow.
Incremental Abstraction in Distributed Probabilistic SLAM Graphs
Scene graphs represent the key components of a scene in a compact and semantically rich way, but are difficult to build during incremental SLAM operation because of the challenges of robustly identifying abstract scene elements and optimising continually changing, complex graphs.
A visual introduction to Gaussian Belief Propagation
In this article, we present a visual introduction to Gaussian Belief Propagation (GBP), an approximate probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs.
Coordinated hippocampal-entorhinal replay as structural inference
Constructing and maintaining useful representations of sensory experience is essential for reasoning about ones environment.
