978 citations · 1.4k across the 15 of their papers we have counts for
8 papers · 1 filter
Achieving and Understanding Out-of-Distribution Generalization in Systematic Reasoning in Small-Scale Transformers
Andrew J. Nam, Mustafa Abdool, Trevor Maxfield +1
Out-of-distribution generalization (OODG) is a longstanding challenge for neural networks. This challenge is quite apparent in tasks with well-defined variables and rules, where ex…
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.…
Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks
Yuxuan Li, James L. McClelland
Transformer networks have seen great success in natural language processing and machine vision, where task objectives such as next word prediction and image classification benefit…
Tell me why! Explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta +8
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of e…
Systematic human learning and generalization from a brief tutorial with explanatory feedback
Andrew J. Nam, James L. McClelland
Neural networks have long been used to model human intelligence, capturing elements of behavior and cognition, and their neural basis. Recent advancements in deep learning have ena…
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…
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…
A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, Surya Ganguli
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, th…