266 citations · 629 across the 25 of their papers we have counts for
7 papers · 1 filter
Contextual Embeddings: When Are They Worth It?
Simran Arora, Avner May, Jian Zhang +1
We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even…
Understanding and Improving Information Transfer in Multi-Task Learning
Sen Wu, Hongyang R. Zhang, Christopher Ré
We investigate multi-task learning approaches that use a shared feature representation for all tasks. To better understand the transfer of task information, we study an architectur…
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Ines Chami, Adva Wolf, Da-Cheng Juan +3
Knowledge graph (KG) embeddings learn low-dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which…
Ivy: Instrumental Variable Synthesis for Causal Inference
Zhaobin Kuang, Frederic Sala, Nimit Sohoni +5
A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x.…
Assessing Robustness to Noise: Low-Cost Head CT Triage
Sarah M. Hooper, Jared A. Dunnmon, Matthew P. Lungren +4
Automated medical image classification with convolutional neural networks (CNNs) has great potential to impact healthcare, particularly in resource-constrained healthcare systems w…
Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski, Avner May, Jian Zhang +3
Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML…