Search Results for author:
Found 7 papers, 4 papers with code
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
In this paper, we present an extension of Transformers where multi-head attention is augmented with two distinct types of attention heads, each routing information of a different type.
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other.
Approximation of relation functions and attention mechanisms
Inner products of neural network feature maps arise in a wide variety of machine learning frameworks as a method of modeling relations between inputs.
Learning Hierarchical Relational Representations through Relational Convolutions
A maturing area of research in deep learning is the study of architectures and inductive biases for learning representations of relational features.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers
An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor.
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL).
