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Found 13 papers, 5 papers with code
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset.
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model.
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions.
Implicit SVD for Graph Representation Learning
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e. g., for calculating gradients via backprop over many data epochs.
On Local Aggregation in Heterophilic Graphs
We also show that homophily is a poor measure of the information in a node's local neighborhood and propose the Neighborhood Information Content(NIC) metric, which is a novel information-theoretic graph metric.
Structured Citation Trend Prediction Using Graph Neural Networks
Academic citation graphs represent citation relationships between publications across the full range of academic fields.
Permutohedral-GCN: Graph Convolutional Networks with Global Attention
The attention coefficients depend on the Euclidean distance between learnable node embeddings, and we show that the resulting attention-based global aggregation scheme is analogous to high-dimensional Gaussian filtering.
Fully Convolutional Graph Neural Networks using Bipartite Graph Convolutions
Graph neural networks have been adopted in numerous applications ranging from learning relational representations to modeling data on irregular domains such as point clouds, social graphs, and molecular structures.
Conditional Graph Neural Processes: A Functional Autoencoder Approach
Thus, we refer to our model as Conditional Graph Neural Process (CGNP).
Hierarchical Bipartite Graph Convolution Networks
Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds and social graphs.
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments.
