Streaming Inference for Infinite Feature Models
Unsupervised learning from a continuous stream of data is arguably one of the most
common and most challenging problems facing intelligent agents. One class of …
common and most challenging problems facing intelligent agents. One class of …
Fragmented spatial maps from surprisal: State abstraction and efficient planning
When animals explore spatial environments, their representations often fragment into
multiple maps. What determines these map fragmentations, and can we predict where they …
multiple maps. What determines these map fragmentations, and can we predict where they …
Associative Memory Under the Probabilistic Lens: Improved Transformers & Dynamic Memory Creation
Clustering is a fundamental unsupervised learning problem, and recent work showed
modern continuous associative memory (AM) networks can learn to cluster data via a novel …
modern continuous associative memory (AM) networks can learn to cluster data via a novel …
Streaming Inference for Infinite Non-Stationary Clustering
Learning from a continuous stream of non-stationary data in an unsupervised manner is
arguably one of the most common and most challenging settings facing intelligent agents …
arguably one of the most common and most challenging settings facing intelligent agents …