activity
20192021
most citedLearning Invariant Representations of Social Media Users

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

cs.SI2021

A Deep Metric Learning Approach to Account Linking

Aleem Khan, Elizabeth Fleming, Noah Schofield +2

We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding documen…

cs.LG2020

Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three

Steven Reich, David Mueller, Nicholas Andrews

Modern neural networks do not always produce well-calibrated predictions, even when trained with a proper scoring function such as cross-entropy. In classification settings, simple…

cs.CL2020

Sources of Transfer in Multilingual Named Entity Recognition

David Mueller, Nicholas Andrews, Mark Dredze

Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one m…

cs.CL2020

Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

Mitchell A. Gordon, Kevin Duh, Nicholas Andrews

Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models…

cs.SI20193 cited

Learning Invariant Representations of Social Media Users

Nicholas Andrews, Marcus Bishop

The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naïve ap…