关于持续学习的顺序贝叶斯推理
Samuel Kessler1, Adam Cobb2, Tim G J Rudner3
1Department of Engineering Science, University of Oxford, Oxford OX2 6ED, UK.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
序列贝叶斯推理在贝叶斯神经网络中与灾难性遗忘作斗争. 一个新的原型贝叶斯持续学习基线显示了计算机视觉基准的竞争性表现.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 持续学习的目的是培训模型进行连续的任务,而不会忘记过去的知识.
- 序列贝叶斯推理提供了一个理论框架,通过使用先前的后者作为信息的前者来进行持续学习.
- 贝叶斯神经网络因其概率性而被探索其在持续学习中的潜力.
研究的目的:
- 评估序列贝叶斯推理在贝叶斯神经网络中防止灾难性遗忘的有效性.
- 研究在神经网络中应用顺序贝叶斯推理与哈密尔顿蒙特卡洛的挑战.
- 提出和评估一个新的贝叶斯持续学习方法.
主要方法:
- 使用哈密尔顿式蒙特卡洛进行了序列贝叶斯推理.
- 在哈密尔顿式蒙特卡洛样本上使用密度估计器近似计算后部分布.
- 用分析示例来研究模型错误规范和数据不平衡的影响.
- 提出并评估了一种新方法,即原型贝叶斯持续学习.
主要成果:
- 直接应用顺序贝叶斯推理与哈密尔顿蒙特卡洛推理未能防止贝叶斯神经网络的灾难性遗忘.
- 模型错误规范和数据不平衡被确定为持续学习中的重大挑战.
- 拟议的原型贝叶斯持续学习基线在类增量计算机视觉基准上取得了竞争性结果.
结论:
- 对神经网络权重的顺序贝叶斯推理对于强大的持续学习是不够的.
- 需要持续学习生成过程的概率模型.
- 原型贝叶斯持续学习为课堂增量持续学习提供了一个有希望和有效的方法.
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