526 citations · 1.2k across the 20 of their papers we have counts for
8 papers · 1 filter
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…
Learning and Evaluating General Linguistic Intelligence
Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor +8
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to…
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst +24
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, i…
Fast Parametric Learning with Activation Memorization
Jack W Rae, Chris Dyer, Peter Dayan +1
Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, s…
Learning Deep Generative Models of Graphs
Yujia Li, Oriol Vinyals, Chris Dyer +2
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interact…