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07/14/2023
Training Discrete Energy-Based Models with Energy Discrepancy
Training energy-based models (EBMs) on discrete spaces is challenging be...
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07/12/2023
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Energy-based models are a simple yet powerful class of probabilistic mod...
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03/04/2023
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Studies have shown that modern neural networks tend to be poorly calibra...
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03/04/2023
Calibrating Transformers via Sparse Gaussian Processes
Transformer models have achieved profound success in prediction tasks in...
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02/21/2023
Scalable Infomin Learning
The task of infomin learning aims to learn a representation with high ut...
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07/12/2022
Markovian Gaussian Process Variational Autoencoders
Deep generative models are widely used for modelling high-dimensional ti...
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07/11/2022
Repairing Neural Networks by Leaving the Right Past Behind
Prediction failures of machine learning models often arise from deficien...
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03/03/2022
Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference
Learning set functions becomes increasingly more important in many appli...
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09/25/2021
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
Machine learning models commonly exhibit unexpected failures post-deploy...
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07/21/2021
Interpreting diffusion score matching using normalizing flow
Scoring matching (SM), and its related counterpart, Stein discrepancy (S...
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02/05/2021
Active Slices for Sliced Stein Discrepancy
Sliced Stein discrepancy (SSD) and its kernelized variants have demonstr...
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01/26/2021
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Semi-supervised learning through deep generative models and multi-lingua...
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11/02/2020
Reinforcement Learning with Efficient Active Feature Acquisition
Solving real-life sequential decision making problems under partial obse...
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10/28/2020
A Study on Efficiency in Continual Learning Inspired by Human Learning
Humans are efficient continual learning systems; we continually learn ne...
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09/25/2020
Hierarchical Sparse Variational Autoencoder for Text Encoding
In this paper we focus on unsupervised representation learning and propo...
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07/24/2020
Interpreting Spatially Infinite Generative Models
Traditional deep generative models of images and other spatial modalitie...
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07/06/2020
Meta-Learning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesi...
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06/30/2020
Sliced Kernelized Stein Discrepancy
Kernelized Stein discrepancy (KSD), though being extensively used in goo...
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05/03/2020
A Causal View on Robustness of Neural Networks
We present a causal view on the robustness of neural networks against in...
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02/28/2020
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
Recent work has shown the ability to learn generative models for 3D shap...
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10/28/2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
The ability for policies to generalize to new environments is key to the...
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09/30/2019
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Variational Autoencoders (VAEs) are known to suffer from learning uninfo...
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09/02/2019
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
Neural networks provide state-of-the-art performance on a variety of tas...
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06/27/2019
'In-Between' Uncertainty in Bayesian Neural Networks
We describe a limitation in the expressiveness of the predictive uncerta...
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05/07/2019
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
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06/12/2018
Meta-Learning for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has become increa...
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06/06/2018
Variational Implicit Processes
This paper introduces the variational implicit processes (VIPs), a Bayes...
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03/08/2018
A Deep Generative Model for Disentangled Representations of Sequential Data
We present a VAE architecture for encoding and generating high dimension...
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02/19/2018
Are Generative Classifiers More Robust to Adversarial Attacks?
There is a rising interest in studying the robustness of deep neural net...
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10/29/2017
Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but g...
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05/19/2017
Gradient Estimators for Implicit Models
Implicit models, which allow for the generation of samples but not for p...
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03/08/2017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
To obtain uncertainty estimates with real-world Bayesian deep learning m...
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02/27/2017
Approximate Inference with Amortised MCMC
We propose a novel approximate inference algorithm that approximates a t...
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02/12/2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
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02/06/2016
Rényi Divergence Variational Inference
This paper introduces the variational Rényi bound (VR) that extends trad...
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11/11/2015
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
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11/10/2015
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
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11/10/2015
Black-box α-divergence Minimization
Black-box alpha (BB-α) is a new approximate inference method based on th...
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06/12/2015
