Nick Pawlowski

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Imperial College London ∙ Rasa

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Deep Structural Causal Models for Tractable Counterfactual Inference

We formulate a general framework for building structural causal models (...
06/11/2020 ∙ by Nick Pawlowski, et al. ∙ 48 ∙ share

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Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

In image segmentation, there is often more than one plausible solution f...
06/10/2020 ∙ by Miguel Monteiro, et al. ∙ 32 ∙ share

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An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

There are several benefits from learning disentangled representations, i...
01/24/2020 ∙ by Rujikorn Charakorn, et al. ∙ 5 ∙ share

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Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

One of the biggest challenges for deep learning algorithms in medical im...
08/21/2019 ∙ by Qingjie Meng, et al. ∙ 4 ∙ share

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Needles in Haystacks: On Classifying Tiny Objects in Large Images

In some computer vision domains, such as medical or hyperspectral imagin...
08/16/2019 ∙ by Nick Pawlowski, et al. ∙ 6 ∙ share

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Is Texture Predictive for Age and Sex in Brain MRI?

Deep learning builds the foundation for many medical image analysis task...
07/25/2019 ∙ by Nick Pawlowski, et al. ∙ 4 ∙ share

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Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

Recent advances in deep learning led to novel generative modeling techni...
06/14/2018 ∙ by Xiaoran Chen, et al. ∙ 4 ∙ share

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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

NeuroNet is a deep convolutional neural network mimicking multiple popul...
06/11/2018 ∙ by Martin Rajchl, et al. ∙ 8 ∙ share

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Rasa: Open Source Language Understanding and Dialogue Management

We introduce a pair of tools, Rasa NLU and Rasa Core, which are open sou...
12/14/2017 ∙ by Tom Bocklisch, et al. ∙ 0 ∙ share

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DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

We present DLTK, a toolkit providing baseline implementations for effici...
11/18/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consiste...
11/04/2017 ∙ by Konstantinos Kamnitsas, et al. ∙ 0 ∙ share

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Implicit Weight Uncertainty in Neural Networks

We interpret HyperNetworks within the framework of variational inference...
11/03/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

The problem of sparse rewards is one of the hardest challenges in contem...
05/18/2017 ∙ by Nat Dilokthanakul, et al. ∙ 0 ∙ share

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Efficient variational Bayesian neural network ensembles for outlier detection

In this work we perform outlier detection using ensembles of neural netw...
03/20/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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