7 citations · 11 across the 6 of their papers we have counts for
7 papers
Clustering Running Titles to Understand the Printing of Early Modern Books
Nikolai Vogler, Kartik Goyal, Samuel V. Lemley +4
We propose a novel computational approach to automatically analyze the physical process behind printing of early modern letterpress books via clustering the running titles found at…
Contrastive Attention Networks for Attribution of Early Modern Print
Nikolai Vogler, Kartik Goyal, Kishore PV Reddy +5
In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500--1800) English printed books. Specifically, we focus on matching uniquel…
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,…
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…
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…
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…