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08/10/2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in s...
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07/11/2023
Geometric Neural Diffusion Processes
Denoising diffusion models have proven to be a flexible and effective pa...
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07/06/2023
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over ...
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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...
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04/20/2023
An Introduction to Transformers
The transformer is a neural network component that can be used to learn ...
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03/25/2023
Autoregressive Conditional Neural Processes
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractiv...
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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...
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02/02/2023
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially pr...
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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...
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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...
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10/29/2022
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate. However, before...
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09/23/2022
Differentially private partitioned variational inference
Learning a privacy-preserving model from distributed sensitive data is a...
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09/11/2022
Kernel Learning for Explainable Climate Science
The Upper Indus Basin, Himalayas provides water for 270 million people a...
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09/01/2022
The Neural Process Family: Survey, Applications and Perspectives
The standard approaches to neural network implementation yield powerful ...
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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...
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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...
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05/18/2022
Multi-disciplinary fairness considerations in machine learning for clinical trials
While interest in the application of machine learning to improve healthc...
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03/16/2022
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-lear...
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03/14/2022
Modelling Non-Smooth Signals with Complex Spectral Structure
The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a ...
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02/24/2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
The proliferation of computing devices has brought about an opportunity ...
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07/02/2021
Memory Efficient Meta-Learning with Large Images
Meta learning approaches to few-shot classification are computationally ...
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06/24/2021
Continual Novelty Detection
Novelty Detection methods identify samples that are not representative o...
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06/18/2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Gaussian processes (GPs) are important probabilistic tools for inference...
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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...
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01/20/2021
Convolutional conditional neural processes for local climate downscaling
A new model is presented for multisite statistical downscaling of temper...
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01/10/2021
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of mode...
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11/24/2020
Generalized Variational Continual Learning
Continual learning deals with training models on new tasks and datasets ...
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10/20/2020
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...
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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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07/23/2020
Diagnostic Questions:The NeurIPS 2020 Education Challenge
Digital technologies are becoming increasingly prevalent in education, e...
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07/02/2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probab...
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04/29/2020
Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, ye...
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03/06/2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Modern meta-learning approaches for image classification rely on increas...
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11/26/2019
Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations...
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11/24/2019
Differentially Private Federated Variational Inference
In many real-world applications of machine learning, data are distribute...
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11/21/2019
Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of rela...
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11/14/2019
Scalable Exact Inference in Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...
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10/29/2019
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...
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09/05/2019
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Recently there has been an increased interest in unsupervised learning o...
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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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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...
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06/06/2019
Practical Deep Learning with Bayesian Principles
Bayesian methods promise to fix many shortcomings of deep learning, but ...
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05/24/2019
Fast computation of loudness using a deep neural network
The present paper introduces a deep neural network (DNN) for predicting ...
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05/06/2019
Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially. T...
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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...
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11/15/2018
Infinite-Horizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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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...
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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...
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05/22/2018
