I am a Ph.D. student in statistical machine learning at the University of Oxford, supervised by Yee Whye Teh and Tom Rainforth. I mainly work on meta learning and equivariance in deep learning. For more information, please see my personal website.
Subsampling is used in convolutional neural networks (CNNs) in the form of pooling or strided convolutions, to reduce the spatial dimensions of feature maps and to allow the receptive fields to grow exponentially with depth. However, it is known that such subsampling operations are not translation equivariant, unlike convolutions that are translation equivariant. Here, we first introduce translation equivariant subsampling/upsampling layers that can be used to construct exact translation equivariant CNNs. We then generalise these layers beyond translations to general groups, thus proposing group equivariant subsampling/upsampling. We use these layers to construct group equivariant autoencoders (GAEs) that allow us to learn low-dimensional equivariant representations. We empirically verify on images that the representations are indeed equivariant to input translations and rotations, and thus generalise well to unseen positions and orientations. We further use GAEs in models that learn object-centric representations on multi-object datasets, and show improved data efficiency and decomposition compared to non-equivariant baselines.
@inproceedings{xu2021group,
title = {Group Equivariant Subsampling},
author = {Xu, Jin and Kim, Hyunjik and Rainforth, Tom and Teh, Yee Whye},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2021}
}
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.
@inproceedings{xu2019metafun,
title = {MetaFun: Meta-Learning with Iterative Functional Updates},
author = {Xu, Jin and Ton, Jean-Francois and Kim, Hyunjik and Kosiorek, Adam R and Teh, Yee Whye},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2020}
}
