45 citations · 126 across the 10 of their papers we have counts for
8 papers · 1 filter
Modular Networks: Learning to Decompose Neural Computation
Louis Kirsch, Julius Kunze, David Barber
Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Condi…
Stochastic Variational Optimization
Thomas Bird, Julius Kunze, David Barber
Variational Optimization forms a differentiable upper bound on an objective. We show that approaches such as Natural Evolution Strategies and Gaussian Perturbation, are special cas…
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Zhen He, Jian Li, Daxue Liu +2
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based o…
Generating Sentences Using a Dynamic Canvas
Harshil Shah, Bowen Zheng, David Barber
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attenti…
Generative Neural Machine Translation
Harshil Shah, David Barber
We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an…
Improving latent variable descriptiveness with AutoGen
Alex Mansbridge, Roberto Fierimonte, Ilya Feige +1
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often…