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Unpacking Intuition
Can intuition be taught? The way in which faces are recognized, the structure of natural classes, and the architecture of intuition may all be instances of the same process. The conjecture that intuition is a species of recognition memory implies ...
Generative Agents: Interactive Simulacra of Human Behavior
The content discusses generative agents that simulate believable human behavior for interactive applications. These agents populate a sandbox environment, interact with each other, plan their days, form relationships, and exhibit emergent social behaviors. The paper introduces a novel architecture that allows agents to remember, retrieve, reflect, and interact dynamically.
Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories
Diffusion Models and Associative Memories show surprising similarities in their mathematical underpinnings and goals, bridging traditional and modern AI research. This connection highlights the convergence of AI models towards memory-focused paradigms, emphasizing the importance of understanding Associative Memories in the field of computation. By exploring these parallels, researchers aim to enhance our comprehension of how models like Diffusion Models and Transformers operate in Deep Learning applications.
Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories
Diffusion Models (DMs) have become increasingly popular in generating benchmarks, but their mathematical descriptions can be complex. In this survey, the authors provide an overview of DMs from the perspective of dynamical systems and Ordinary Differential Equations (ODEs), revealing a mathematical connection to Associative Memories (AMs). AMs are energy-based models that share similarities with denoising DMs, but they allow for the computation of a Lyapunov energy function and gradient descent to denoise data. The authors also summarize the 40-year history of energy-based AMs, starting with the Hopfield Network, and discuss future research directions for both AMs and DMs.
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