75 citations · 326 across the 20 of their papers we have counts for
3 papers · 1 filter
Distilling Internet-Scale Vision-Language Models into Embodied Agents
Theodore Sumers, Kenneth Marino, Arun Ahuja +2
Instruction-following agents must ground language into their observation and action spaces. Learning to ground language is challenging, typically requiring domain-specific engineer…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw +2
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Ag…