• Publications
  • Influence
Intriguing properties of neural networks
TLDR
It is found that there is no distinction between individual highlevel units and random linear combinations of high level units, according to various methods of unit analysis, and it is suggested that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Expand
Spectral Networks and Locally Connected Networks on Graphs
TLDR
This paper considers possible generalizations of CNNs to signals defined on more general domains without the action of a translation group, and proposes two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. Expand
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
TLDR
Using large state-of-the-art models, this work demonstrates speedups of convolutional layers on both CPU and GPU by a factor of 2 x, while keeping the accuracy within 1% of the original model. Expand
Geometric Deep Learning: Going beyond Euclidean data
TLDR
Deep neural networks are used for solving a broad range of problems from computer vision, natural-language processing, and audio analysis where the invariances of these structures are built into networks used to model them. Expand
Few-Shot Learning with Graph Neural Networks
TLDR
A graph neural network architecture is defined that generalizes several of the recently proposed few-shot learning models and provides improved numerical performance, and is easily extended to variants of few- shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks. Expand
Invariant Scattering Convolution Networks
  • Joan Bruna, S. Mallat
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 5 March 2012
TLDR
The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. Expand
Invariant Scattering Convolution Networks.
TLDR
The mathematical analysis of wavelet scattering networks explain important properties of deep convolution networks for classification. Expand
Deep Convolutional Networks on Graph-Structured Data
TLDR
This paper develops an extension of Spectral Networks which incorporates a Graph Estimation procedure, that is test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate. Expand
Training Convolutional Networks with Noisy Labels
TLDR
An extra noise layer is introduced into the network which adapts the network outputs to match the noisy label distribution and can be estimated as part of the training process and involve simple modifications to current training infrastructures for deep networks. Expand
On the equivalence between graph isomorphism testing and function approximation with GNNs
TLDR
It is proved that order-2 Graph G-invariant networks fail to distinguish non-isomorphic regular graphs with the same degree, and is extended to a new architecture, Ring-GNNs, which succeeds on distinguishing these graphs and provides improvements on real-world social network datasets. Expand
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