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09/07/2023
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
Survival analysis is a valuable tool for estimating the time until speci...
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05/19/2023
Moment Matching Denoising Gibbs Sampling
Energy-Based Models (EBMs) offer a versatile framework for modeling comp...
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05/18/2023
Generalized Multiple Intent Conditioned Slot Filling
Natural language understanding includes the tasks of intent detection (i...
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03/19/2023
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
In this study, we present a hybrid CNN-RNN approach to investigate long-...
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03/15/2023
Smoothed Q-learning
In Reinforcement Learning the Q-learning algorithm provably converges to...
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09/15/2022
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and st...
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06/19/2022
Integrated Weak Learning
We introduce Integrated Weak Learning, a principled framework that integ...
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05/28/2022
Improving VAE-based Representation Learning
Latent variable models like the Variational Auto-Encoder (VAE) are commo...
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05/23/2022
Generalization Gap in Amortized Inference
The ability of likelihood-based probabilistic models to generalize to un...
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03/21/2022
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibroti...
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01/13/2022
Parallel Neural Local Lossless Compression
The recently proposed Neural Local Lossless Compression (NeLLoC), which ...
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11/30/2021
Adaptive Optimization with Examplewise Gradients
We propose a new, more general approach to the design of stochastic grad...
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09/24/2021
Sample Efficient Model Evaluation
Labelling data is a major practical bottleneck in training and testing c...
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03/30/2021
Locally-Contextual Nonlinear CRFs for Sequence Labeling
Linear chain conditional random fields (CRFs) combined with contextual w...
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10/26/2020
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks
Deep generative models provide a powerful set of tools to understand rea...
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10/23/2020
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy
In recent years, the collection and sharing of individuals' private data...
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10/07/2020
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
Probabilistic models with hierarchical-latent-variable structures provid...
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10/07/2020
Learning disentangled representations with the Wasserstein Autoencoder
Disentangled representation learning has undoubtedly benefited from obje...
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04/30/2020
Bayesian Online Meta-Learning with Laplace Approximation
Neural networks are known to suffer from catastrophic forgetting when tr...
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01/14/2020
Private Machine Learning via Randomised Response
We introduce a general learning framework for private machine learning b...
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12/20/2019
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
We make the following striking observation: fully convolutional VAE mode...
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07/27/2019
Variational f-divergence Minimization
Probabilistic models are often trained by maximum likelihood, which corr...
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02/12/2019
Gaussian Mean Field Regularizes by Limiting Learned Information
Variational inference with a factorized Gaussian posterior estimate is a...
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01/15/2019
Practical Lossless Compression with Latent Variables using Bits Back Coding
Deep latent variable models have seen recent success in many data domain...
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11/21/2018
Spread Divergences
For distributions p and q with different support, the divergence general...
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11/13/2018
Modular Networks: Learning to Decompose Neural Computation
Scaling model capacity has been vital in the success of deep learning. F...
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09/13/2018
Stochastic Variational Optimization
Variational Optimization forms a differentiable upper bound on an object...
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09/10/2018
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Online Multi-Object Tracking (MOT) from videos is a challenging computer...
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06/13/2018
Generative Neural Machine Translation
We introduce Generative Neural Machine Translation (GNMT), a latent vari...
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06/13/2018
Generating Sentences Using a Dynamic Canvas
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a...
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06/12/2018
Improving latent variable descriptiveness with AutoGen
Powerful generative models, particularly in Natural Language Modelling, ...
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06/12/2018
Gaussian mixture models with Wasserstein distance
Generative models with both discrete and continuous latent variables are...
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05/20/2018
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
We introduce the Kronecker factored online Laplace approximation for ove...
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11/05/2017
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Long Short-Term Memory (LSTM) is a popular approach to boosting the abil...
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06/12/2017
Practical Gauss-Newton Optimisation for Deep Learning
We present an efficient block-diagonal ap- proximation to the Gauss-Newt...
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05/23/2017
Thinking Fast and Slow with Deep Learning and Tree Search
Sequential decision making problems, such as structured prediction, robo...
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07/07/2016
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
We present a unifying framework for adapting the update direction in gra...
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06/22/2016
Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?
We consider training probabilistic classifiers in the case of a large nu...
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12/18/2012
Variational Optimization
We discuss a general technique that can be used to form a differentiable...
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02/14/2012