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05/24/2023
Deep Stochastic Processes via Functional Markov Transition Operators
We introduce Markov Neural Processes (MNPs), a new class of Stochastic P...
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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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01/30/2022
COIN++: Data Agnostic Neural Compression
Neural compression algorithms are typically based on autoencoders that r...
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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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03/03/2021
COIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of stori...
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02/09/2021
Generative Models as Distributions of Functions
Generative models are typically trained on grid-like data such as images...
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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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06/18/2020
STEER : Simple Temporal Regularization For Neural ODEs
Training Neural Ordinary Differential Equations (ODEs) is often computat...
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06/13/2020
Equivariant Neural Rendering
We propose a framework for learning neural scene representations directl...
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04/02/2019
Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn represe...
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10/08/2018
Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Semantic inpainting is the task of inferring missing pixels in an image ...
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03/31/2018
Joint-VAE: Learning Disentangled Joint Continuous and Discrete Representations
We present a framework for learning disentangled and interpretable joint...
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02/08/2018