53 citations · 67 across the 3 of their papers we have counts for
6 papers
Learning to Reason With Relational Abstractions
Andrew J. Nam, Mengye Ren, Chelsea Finn +1
Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps.…
Transforming task representations to perform novel tasks
Andrew K. Lampinen, James L. McClelland
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero-shot), based on its relationship to previous tasks. Humans can exhib…
Extending Machine Language Models toward Human-Level Language Understanding
James L. McClelland, Felix Hill, Maja Rudolph +2
Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. The human…
Environmental drivers of systematicity and generalization in a situated agent
Felix Hill, Andrew Lampinen, Rosalia Schneider +4
The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here…
Zero-shot task adaptation by homoiconic meta-mapping
Andrew K. Lampinen, James L. McClelland
How can deep learning systems flexibly reuse their knowledge? Toward this goal, we propose a new class of challenges, and a class of architectures that can solve them. The challeng…
One-shot and few-shot learning of word embeddings
Andrew K. Lampinen, James L. McClelland
Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible abilit…