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20172024
most citedQuantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

10 citations · 23 across the 8 of their papers we have counts for

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cs.LG2022★ 10 cited

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal +2

The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy v…

cs.LG2021★ 7 cited

Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings

Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick

While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences,…

cs.LG2020★ 1 cited

A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing

Kartik Goyal, Chris Dyer, Christopher Warren +2

We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into und…

cs.LG2019★ 3 cited

An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search

Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick

Globally normalized neural sequence models are considered superior to their locally normalized equivalents because they may ameliorate the effects of label bias. However, when cons…

cs.LG2017

A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

Kartik Goyal, Graham Neubig, Chris Dyer +1

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, ty…