Richard E. Turner

research

∙ 08/10/2023

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers

Time-dependent partial differential equations (PDEs) are ubiquitous in s...

0 Phillip Lippe, et al. ∙

research

∙ 07/11/2023

Geometric Neural Diffusion Processes

Denoising diffusion models have proven to be a flexible and effective pa...

0 Emile Mathieu, et al. ∙

research

∙ 07/06/2023

Beyond Intuition, a Framework for Applying GPs to Real-World Data

Gaussian Processes (GPs) offer an attractive method for regression over ...

0 Kenza Tazi, et al. ∙

research

∙ 06/23/2023

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

In this paper we explore few-shot imitation learning for control problem...

0 Massimiliano Patacchiola, et al. ∙

research

∙ 04/20/2023

An Introduction to Transformers

The transformer is a neural network component that can be used to learn ...

0 Richard E. Turner, et al. ∙

research

∙ 03/25/2023

Autoregressive Conditional Neural Processes

Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractiv...

0 Wessel P. Bruinsma, et al. ∙

research

∙ 03/23/2023

First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning

In Class-Incremental Learning (CIL) an image classification system is ex...

0 Aristeidis Panos, et al. ∙

research

∙ 02/02/2023

On the Efficacy of Differentially Private Few-shot Image Classification

There has been significant recent progress in training differentially pr...

0 Marlon Tobaben, et al. ∙

research

∙ 11/23/2022

Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners

This paper examines the robustness of deployed few-shot meta-learning sy...

0 Elre T. Oldewage, et al. ∙

research

∙ 11/18/2022

Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement

Deploying environmental measurement stations can be a costly and time-co...

0 Tom R. Andersson, et al. ∙

research

∙ 10/29/2022

Ice Core Dating using Probabilistic Programming

Ice cores record crucial information about past climate. However, before...

0 Aditya Ravuri, et al. ∙

research

∙ 09/23/2022

Differentially private partitioned variational inference

Learning a privacy-preserving model from distributed sensitive data is a...

5 Mikko A. Heikkilä, et al. ∙

research

∙ 09/11/2022

Kernel Learning for Explainable Climate Science

The Upper Indus Basin, Himalayas provides water for 270 million people a...

9 Vidhi Lalchand, et al. ∙

research

∙ 09/01/2022

The Neural Process Family: Survey, Applications and Perspectives

The standard approaches to neural network implementation yield powerful ...

8 Saurav Jha, et al. ∙

research

∙ 06/20/2022

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

Recent years have seen a growth in user-centric applications that requir...

5 Massimiliano Patacchiola, et al. ∙

research

∙ 06/17/2022

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

Modern deep learning systems are increasingly deployed in situations suc...

20 Aliaksandra Shysheya, et al. ∙

research

∙ 05/18/2022

Multi-disciplinary fairness considerations in machine learning for clinical trials

While interest in the application of machine learning to improve healthc...

4 Isabel Chien, et al. ∙

research

∙ 03/16/2022

Practical Conditional Neural Processes Via Tractable Dependent Predictions

Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-lear...

15 Stratis Markou, et al. ∙

research

∙ 03/14/2022

Modelling Non-Smooth Signals with Complex Spectral Structure

The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a ...

0 Wessel P. Bruinsma, et al. ∙

research

∙ 02/24/2022

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

The proliferation of computing devices has brought about an opportunity ...

32 Matthew Ashman, et al. ∙

research

∙ 07/02/2021

Memory Efficient Meta-Learning with Large Images

Meta learning approaches to few-shot classification are computationally ...

10 John Bronskill, et al. ∙

research

∙ 06/24/2021

Continual Novelty Detection

Novelty Detection methods identify samples that are not representative o...

5 Rahaf Aljundi, et al. ∙

research

∙ 06/18/2021

Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes

Gaussian processes (GPs) are important probabilistic tools for inference...

8 Will Tebbutt, et al. ∙

research

∙ 06/07/2021

How Tight Can PAC-Bayes be in the Small Data Regime?

In this paper, we investigate the question: Given a small number of data...

0 Andrew Y. K. Foong, et al. ∙

research

∙ 01/20/2021

Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temper...

16 Anna Vaughan, et al. ∙

research

∙ 01/10/2021

The Gaussian Neural Process

Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of mode...

48 Wessel P. Bruinsma, et al. ∙

research

∙ 11/24/2020

Generalized Variational Continual Learning

Continual learning deals with training models on new tasks and datasets ...

7 Noel Loo, et al. ∙

research

∙ 10/20/2020

Sparse Gaussian Process Variational Autoencoders

Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...

16 Matthew Ashman, et al. ∙

research

∙ 07/24/2020

Interpreting Spatially Infinite Generative Models

Traditional deep generative models of images and other spatial modalitie...

6 Chaochao Lu, et al. ∙

research

∙ 07/23/2020

Diagnostic Questions:The NeurIPS 2020 Education Challenge

Digital technologies are becoming increasingly prevalent in education, e...

0 Zichao Wang, et al. ∙

research

∙ 07/02/2020

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Stationary stochastic processes (SPs) are a key component of many probab...

2 Andrew Y. K. Foong, et al. ∙

research

∙ 04/29/2020

Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, ye...

17 Pingbo Pan, et al. ∙

research

∙ 03/06/2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increas...

12 John Bronskill, et al. ∙

research

∙ 11/26/2019

Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling

Dialogue systems benefit greatly from optimizing on detailed annotations...

0 Bo-Hsiang Tseng, et al. ∙

research

∙ 11/24/2019

Differentially Private Federated Variational Inference

In many real-world applications of machine learning, data are distribute...

16 Mrinank Sharma, et al. ∙

research

∙ 11/21/2019

Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of rela...

20 Tameem Adel, et al. ∙

research

∙ 11/14/2019

Scalable Exact Inference in Multi-Output Gaussian Processes

Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...

15 Wessel P. Bruinsma, et al. ∙

research

∙ 10/29/2019

Convolutional Conditional Neural Processes

We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...

45 Jonathan Gordon, et al. ∙

research

∙ 09/05/2019

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Recently there has been an increased interest in unsupervised learning o...

24 Jan Stühmer, et al. ∙

research

∙ 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...

5 Andrew Y. K. Foong, et al. ∙

research

∙ 06/27/2019

'In-Between' Uncertainty in Bayesian Neural Networks

We describe a limitation in the expressiveness of the predictive uncerta...

1 Andrew Y. K. Foong, et al. ∙

research

∙ 06/18/2019

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

The goal of this paper is to design image classification systems that, a...

6 James Requeima, et al. ∙

research

∙ 06/06/2019

Practical Deep Learning with Bayesian Principles

Bayesian methods promise to fix many shortcomings of deep learning, but ...

23 Kazuki Osawa, et al. ∙

research

∙ 05/24/2019

Fast computation of loudness using a deep neural network

The present paper introduces a deep neural network (DNN) for predicting ...

0 Josef Schlittenlacher, et al. ∙

research

∙ 05/06/2019

Improving and Understanding Variational Continual Learning

In the continual learning setting, tasks are encountered sequentially. T...

12 Siddharth Swaroop, et al. ∙

research

∙ 11/27/2018

Partitioned Variational Inference: A unified framework encompassing federated and continual learning

Variational inference (VI) has become the method of choice for fitting m...

0 Thang D. Bui, et al. ∙

research

∙ 11/15/2018

Infinite-Horizon Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removin...

8 Arno Solin, et al. ∙

research

∙ 10/09/2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Bayesian neural networks (BNNs) hold great promise as a flexible and pri...

6 Anqi Wu, et al. ∙

research

∙ 05/24/2018

Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning

This paper develops a general framework for data efficient and versatile...

0 Jonathan Gordon, et al. ∙

research

∙ 05/22/2018

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Nonlinear ICA is a fundamental problem for unsupervised representation l...

0 Aapo Hyvärinen, et al. ∙

Richard E. Turner

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