2.7k citations · 6.8k across the 64 of their papers we have counts for
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Stochastic Collapsed Variational Inference for Sequential Data
Pengyu Wang, Phil Blunsom
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variat…
Stochastic Collapsed Variational Inference for Hidden Markov Models
Pengyu Wang, Phil Blunsom
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variat…
Neural Variational Inference for Text Processing
Yishu Miao, Lei Yu, Phil Blunsom
Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for…
Reasoning about Entailment with Neural Attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann +2
While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pi…
A Bayesian Model for Generative Transition-based Dependency Parsing
Jan Buys, Phil Blunsom
We propose a simple, scalable, fully generative model for transition-based dependency parsing with high accuracy. The model, parameterized by Hierarchical Pitman-Yor Processes, ove…
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette +4
Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents…
Learning to Transduce with Unbounded Memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman +1
Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems. In this paper we explore the representational power of…