2.3k citations · 4.7k across the 88 of their papers we have counts for
14 papers · 1 filter
Multimodal Generative Models for Compositional Representation Learning
Mike Wu, Noah Goodman
As deep neural networks become more adept at traditional tasks, many of the most exciting new challenges concern multimodality---observations that combine diverse types, such as im…
Characterizing the dynamics of learning in repeated reference games
Robert D. Hawkins, Michael C. Frank, Noah D. Goodman
The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural…
Continual adaptation for efficient machine communication
Robert D. Hawkins, Minae Kwon, Dorsa Sadigh +1
To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conv…
Shaping Visual Representations with Language for Few-shot Classification
Jesse Mu, Percy Liang, Noah Goodman
By describing the features and abstractions of our world, language is a crucial tool for human learning and a promising source of supervision for machine learning models. We use la…
Analyzing machine-learned representations: A natural language case study
Ishita Dasgupta, Demi Guo, Samuel J. Gershman +1
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representations and decision rules they le…
Learning to Explain: Answering Why-Questions via Rephrasing
Allen Nie, Erin D. Bennett, Noah D. Goodman
Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction. Explanations are challenging in that they require ma…