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Found 11 papers, 7 papers with code
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
On the other hand, shallow (or node-level) models using ego features and adjacency embeddings work well in heterophilous graphs.
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis.
DDXPlus: A New Dataset For Automatic Medical Diagnosis
In this work, we present a large-scale synthetic dataset of roughly 1. 3 million patients that includes a differential diagnosis, along with the ground truth pathology, symptoms and antecedents for each patient.
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible?
Out-of-Sample Representation Learning for Knowledge Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
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Out-of-Sample Representation Learning for Multi-Relational Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.
Diachronic Embedding for Temporal Knowledge Graph Completion
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
Representation Learning for Dynamic Graphs: A Survey
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance.
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks.
