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Found 18 papers, 10 papers with code
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
Long-range interactions are essential for the correct description of complex systems in many scientific fields.
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Existing biased and unbiased MD simulations, however, are prone to miss either rare events or extrapolative regions -- areas of the configurational space where unreliable predictions are made.
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
In the case of alanine dipeptide, by tuning the only free parameter of our loss definition, we find a good correspondence between it and the autocorrelation times, resulting in a $>100$ fold speed up in optimization of simulation parameters compared to a grid-search.
Investigating the Interplay between Features and Structures in Graph Learning
Our findings reveal that previously defined metrics are not adequate when we relax the above assumption.
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features.
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries.
Bayesian Deep Learning for Graphs
In this thesis, we take a different route and develop a Bayesian Deep Learning framework for graph learning.
The Infinite Contextual Graph Markov Model
The Contextual Graph Markov Model is a deep, unsupervised, and probabilistic model for graphs that is trained incrementally on a layer-by-layer basis.
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario.
Graph Mixture Density Networks
We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology.
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
The method relies on deep graph networks, which provide extreme flexibility in the input format.
Concept Matching for Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Theoretically Expressive and Edge-aware Graph Learning
We propose a new Graph Neural Network that combines recent advancements in the field.
A Gentle Introduction to Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.
A Fair Comparison of Graph Neural Networks for Graph Classification
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
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Graph Classification
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Soft Token Matching for Interpretable Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017.
