7 citations · 21 across the 8 of their papers we have counts for
9 papers
Unlocking Out-of-Distribution Generalization in Transformers via Recursive Latent Space Reasoning
Awni Altabaa, Siyu Chen, John Lafferty +1
Systematic, compositional generalization beyond the training distribution remains a core challenge in machine learning -- and a critical bottleneck for the emergent reasoning abili…
CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision
Awni Altabaa, Omar Montasser, John Lafferty
Learning complex functions that involve multi-step reasoning poses a significant challenge for standard supervised learning from input-output examples. Chain-of-thought (CoT) super…
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
Awni Altabaa, John Lafferty
Relational reasoning is a central component of generally intelligent systems, enabling robust and data-efficient inductive generalization. Recent empirical evidence shows that many…
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
Awni Altabaa, Zhuoran Yang
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. Classical…
Approximation of relation functions and attention mechanisms
Awni Altabaa, John Lafferty
Inner products of neural network feature maps arise in a wide variety of machine learning frameworks as a method of modeling relations between inputs. This work studies the approxi…
Learning Hierarchical Relational Representations through Relational Convolutions
Awni Altabaa, John Lafferty
An evolving area of research in deep learning is the study of architectures and inductive biases that support the learning of relational feature representations. In this paper, we…
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
Taylor W. Webb, Steven M. Frankland, Awni Altabaa +9
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between conne…
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers
Awni Altabaa, Taylor Webb, Jonathan Cohen +1
An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor. At the core of the Abstractor is a variant of atte…
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Awni Altabaa, Bora Yongacoglu, Serdar Yüksel
Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL). Recent advances in MARL have focused primarily on games with finitely many states.…