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Found 6 papers, 2 papers with code
Representing Long-Range Context for Graph Neural Networks with Global Attention
Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module.
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines
In particular, we show that the "dot-product attention" that is the core of the Transformer's operation can be characterized as a kernel learning method on a pair of Banach spaces.
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning
Many potential applications of reinforcement learning in the real world involve interacting with other agents whose numbers vary over time.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable.
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies.
Neurons and Cognition Quantitative Methods
