发表机构
University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AIBL将实例学习扩展到神经嵌入空间，通过活跃、遗忘和惊喜记忆结构及毕业算法处理高维顺序数据，在多个任务上准确率提升6-17个百分点。
AI 中文摘要
顺序学习系统通常根据累积的经验做出决策，同时接收可能随时间变化分布的高维输入。实例学习理论（IBLT）通过存储的情境-决策-效用实例、部分匹配、激活和混合，为此类设置提供了一个基于案例的原则性框架。IBLT依赖于字典格式的符号知识表示，但文本、图像、交易向量和用户-项目历史通常需要学习的相似性，而不是手工指定的匹配规则。在本文中，我们引入了AIBL（增强实例学习），一种在高维顺序数据的学习向量空间中制定的实例学习模型。AIBL将符号情境匹配推广到神经嵌入相似性，同时保留实例存储、激活加权检索和效用混合。AIBL模型将记忆组织为活跃、遗忘和惊喜存储。惊喜记忆将弱匹配、可能的分布外或角落案例观察与活跃记忆分开，减少对最近可用案例的强制拟合。一个观察驱动的毕业算法将重复出现的惊喜实例提升到活跃记忆，允许记忆纳入概念漂移下可能出现的重复新颖模式。我们在五个机器学习任务和三个受控模拟任务上评估了相同的实现，将AIBL与经典IBLT变体和任务特定基线（如适用）进行比较。AIBL将准确率提高了6到17个百分点。结果显示了向量空间检索在哪些方面优于符号匹配，以及添加的记忆机制如何在测试协议下控制新颖性检测、冷启动处理、漂移适应和奖励学习。
英文摘要
Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over time. Instance-Based Learning Theory (IBLT) provides a principled case-based framework for such settings through stored situation-decision-utility instances, partial matching, activation, and blending. IBLT relies on symbolic knowledge representation in dictionary-like formats, but text, images, transaction vectors, and user-item histories often require learned similarity rather than hand-specified matching rules. In this paper, we introduce AIBL (Augmented Instance-Based Learning), an instance-learning model formulated in a learned vector space for high- dimensional sequential data. AIBL generalizes symbolic situation matching to neural embedding similarity while retaining instance storage, activation- weighted retrieval, and utility blending. The AIBL model organizes memory into active, forgotten, and surprise stores. Surprise memory separates weakly matched, possible out-of-distribution, or corner-case observations from active memory, reducing forced fitting to the nearest available cases. An observation-driven graduation algorithm promotes recurring surprise instances to active memory, allowing the memory to incorporate repeated novel patterns that may arise under concept drift. We evaluate the same implementation on five machine learning tasks and three controlled simulation tasks, comparing AIBL with classical IBLT variants and task-specific baselines where appropriate. AIBL improves accuracy by 6 to 17 percentage points. The results show where vector-space retrieval improves over symbolic matching and how the added memory mechanisms govern novelty detection, cold-start handling, drift adaptation, and reward learning under the tested protocols.
