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Found 8 papers, 5 papers with code
Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily.
Reinforcement Learning Textbook
This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics.
Combining Reward and Rank Signals for Slate Recommendation
In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.
Are Hyperbolic Representations in Graphs Created Equal?
Recently there was an increasing interest in applications of graph neural networks in non-Euclidean geometry; however, are non-Euclidean representations always useful for graph learning tasks?
Modern Deep Reinforcement Learning Algorithms
Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research.
Unsupervised Community Detection with Modularity-Based Attention Model
In this paper we take a problem of unsupervised nodes clustering on graphs and show how recent advances in attention models can be applied successfully in a "hard" regime of the problem.
Anonymous Walk Embeddings
The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data.
Ranked #17 on
Graph Classification
on IMDb-B
