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- Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh
- Neural Computation
- 2006

We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected… (More)

- Yannis Kalantidis, Clayton Mellina, Simon Osindero
- ECCV Workshops
- 2016

We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific… (More)

- Mehdi Mirza, Simon Osindero
- ArXiv
- 2014

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits… (More)

- Edward Meeds, Simon Osindero
- NIPS
- 2005

We present an infinite mixture model in which each component comprises a multivariate Gaussian distribution over an input space, and a Gaussian Process model over an output space. Our model is neatly able to deal with non-stationary covariance functions, discontinuities, multi-modality and overlapping output signals. The work is similar to that by Rasmussen… (More)

- Simon Osindero, Geoffrey E. Hinton
- NIPS
- 2007

We describe an efficient learning procedure for multilayer generative models that combine the best aspects of Markov random fields and deep, directed belief nets. The generative models can be learned one layer at a time and when learning is complete they have a very fast inference procedure for computing a good approximation to the posterior distribution in… (More)

- Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh
- Cognitive Science
- 2006

We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit… (More)

- Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton
- Journal of Machine Learning Research
- 2003

We present a new way of extending independent components analysis (ICA) to overcomplete representations. In contrast to the causal generative extensions of ICA which maintain marginal independence of sources, we define features as deterministic (linear) functions of the inputs. This assumption results in marginal dependencies among the features, but… (More)

- Max Welling, Geoffrey E. Hinton, Simon Osindero
- NIPS
- 2002

We propose a model for natural images in which the probability of an image is proportional to the product of the probabilities of some filter outputs. We encourage the system to find sparse features by using a Student-t distribution to model each filter output. If the t-distribution is used to model the combined outputs of sets of neurally adjacent filters,… (More)

- Simon Osindero, Max Welling, Geoffrey E. Hinton
- Neural Computation
- 2006

We present an energy-based model that uses a product of generalized Student-t distributions to capture the statistical structure in data sets. This model is inspired by and particularly applicable to "natural" data sets such as images. We begin by providing the mathematical framework, where we discuss complete and overcomplete models and provide algorithms… (More)

- Max Jaderberg, Wojciech Czarnecki, +4 authors Koray Kavukcuoglu
- ICML
- 2017

Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates. All layers, or more generally, modules, of the network are therefore locked, in the sense that they must wait for the remainder of the network to execute forwards and propagate error… (More)