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Found 11 papers, 10 papers with code
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay.
ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups
Surfing on the success of graph- and group-based neural networks, we take advantage of the recent developments in the geometric deep learning field to derive a new approach to exploit any anisotropies in data.
Learning to recover orientations from projections in single-particle cryo-EM
Our approach consists of two steps: (i) the estimation of distances between pairs of projections, and (ii) the recovery of the orientation of each projection from these distances.
DeepSphere: a graph-based spherical CNN
DeepSphere, a method based on a graph representation of the sampled sphere, strikes a controllable balance between these two desiderata.
Simplicial Neural Networks
We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes.
DeepSphere: towards an equivariant graph-based spherical CNN
Spherical data is found in many applications.
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere.
Learning to Recognize Musical Genre from Audio
We here summarize our experience running a challenge with open data for musical genre recognition.
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data.
FMA: A Dataset For Music Analysis
We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.
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Skeleton Based Action Recognition
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