Andrew K. Lampinen | DeepAI

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∙ 06/07/2023

Improving neural network representations using human similarity judgments

Deep neural networks have reached human-level performance on many comput...

0 Lukas Muttenthaler, et al. ∙

∙ 10/11/2022

Transformers generalize differently from information stored in context vs in weights

Transformer models can use two fundamentally different kinds of informat...

0 Stephanie C. Y. Chan, et al. ∙

∙ 07/14/2022

Language models show human-like content effects on reasoning

Abstract reasoning is a key ability for an intelligent system. Large lan...

9 Ishita Dasgupta, et al. ∙

∙ 06/16/2022

Know your audience: specializing grounded language models with the game of Dixit

Effective communication requires adapting to the idiosyncratic common gr...

0 Aaditya K. Singh, et al. ∙

∙ 04/08/2022

Semantic Exploration from Language Abstractions and Pretrained Representations

Continuous first-person 3D environments pose unique exploration challeng...

0 Allison C. Tam, et al. ∙

∙ 04/05/2022

Can language models learn from explanations in context?

Large language models can perform new tasks by adapting to a few in-cont...

2 Andrew K. Lampinen, et al. ∙

∙ 03/15/2022

Zipfian environments for Reinforcement Learning

As humans and animals learn in the natural world, they encounter distrib...

0 Stephanie C. Y. Chan, et al. ∙

∙ 12/07/2021

Tell me why! – Explanations support learning of relational and causal structure

Explanations play a considerable role in human learning, especially in a...

12 Andrew K. Lampinen, et al. ∙

∙ 06/22/2020

What shapes feature representations? Exploring datasets, architectures, and training

In naturalistic learning problems, a model's input contains a wide range...

0 Katherine L. Hermann, et al. ∙

∙ 05/08/2020

Transforming task representations to allow deep learning models to perform novel tasks

An important aspect of intelligence is the ability to adapt to a novel t...

6 Andrew K. Lampinen, et al. ∙

∙ 09/27/2019

Automated curricula through setter-solver interactions

Reinforcement learning algorithms use correlations between policies and ...

0 Sebastien Racanière, et al. ∙

∙ 05/23/2019

Embedded Meta-Learning: Toward more flexible deep-learning models

How can deep learning systems flexibly reuse their knowledge? Toward thi...

0 Andrew K. Lampinen, et al. ∙

∙ 09/27/2018

An analytic theory of generalization dynamics and transfer learning in deep linear networks

Much attention has been devoted recently to the generalization puzzle in...

8 Andrew K. Lampinen, et al. ∙

∙ 10/27/2017

One-shot and few-shot learning of word embeddings

Standard deep learning systems require thousands or millions of examples...

0 Andrew K. Lampinen, et al. ∙

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