神经网络中的随机加权神经调制促进跨任务共同流形的学习
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks
AI总结：
针对监督学习任务间流形几何关系研究不足的问题，定义了任务特定几何敏感哈希（T-GSH），并证明带神经调制系统的随机加权神经网络能实现该功能以学习跨任务流形。
摘要：
几何敏感哈希函数是局部敏感哈希函数的一类，是监督学习中学习特定类别流形几何的神经网络模型。然而，给定一组监督学习任务，理解能够表示每个任务的流形几何以及基于此的任务间关系类型却鲜受关注。我们通过考虑一个生成过程来探索该问题的形式化，其中每个任务与一个高维流形相关联，这可以在具有神经调制系统的类脑模型中实现。基于此公式，我们定义了任务特定几何敏感哈希（T-GSH），并展示了带有神经调制系统的随机加权神经网络能够实现此功能。
英文摘要：
Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning. However, given a set of supervised learning tasks, understanding the manifold geometries that can represent each task and the kinds of relationships between the tasks based on them has received little attention. We explore a formalization of this question by considering a generative process where each task is associated with a high-dimensional manifold, which can be done in brain-like models with neuromodulatory systems. Following this formulation, we define \emph{Task-specific Geometric Sensitive Hashing~(T-GSH)} and show that a randomly weighted neural network with a neuromodulation system can realize this function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
