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02/06/2023
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Neural fields, also known as implicit neural representations, have emerg...
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06/20/2022
When Does Re-initialization Work?
Re-initializing a neural network during training has been observed to im...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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01/28/2022
From data to functa: Your data point is a function and you should treat it like one
It is common practice in deep learning to represent a measurement of the...
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06/10/2021
Group Equivariant Subsampling
Subsampling is used in convolutional neural networks (CNNs) in the form ...
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12/20/2020
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group i...
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06/26/2020
Conditional Set Generation with Transformers
A set is an unordered collection of unique elements–and yet many machine...
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06/08/2020
The Lipschitz Constant of Self-Attention
Lipschitz constants of neural networks have been explored in various con...
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12/05/2019
MetaFun: Meta-Learning with Iterative Functional Updates
Few-shot supervised learning leverages experience from previous learning...
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03/28/2019
Meta-Learning surrogate models for sequential decision making
Meta-learning methods leverage past experience to learn data-driven indu...
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01/17/2019
Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...
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06/05/2018
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable de...
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02/16/2018
Disentangling by Factorising
We define and address the problem of unsupervised learning of disentangl...
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06/08/2017
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Automating statistical modelling is a challenging problem that has far-r...
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05/23/2016