2.3k citations · 3.3k across the 58 of their papers we have counts for
4 papers · 1 filter
GIO: Gradient Information Optimization for Training Dataset Selection
Dante Everaert, Christopher Potts
It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer exa…
Inducing Causal Structure for Interpretable Neural Networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu +5
In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashio…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Relational reasoning and generalization using non-symbolic neural networks
Atticus Geiger, Alexandra Carstensen, Michael C. Frank +1
The notion of equality (identity) is simple and ubiquitous, making it a key case study for broader questions about the representations supporting abstract relational reasoning. Pre…