DeepGOPlus推断的数值稳定性是什么
Inés Gonzalez Pepe1, Yohan Chatelain1, Gregory Kiar2
1Department of Computer Science and Software Engineering, Concordia University, Montreal, Qc, Canada.
PloS one
|January 29, 2024
概括
DeepGOPlus是一种用于蛋白质功能预测的卷积神经网络 (CNN),表现出强大的数值稳定性. 虽然它通常是可靠的,但它与低精度浮点格式用于推理的使用是有选择的.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习是机器学习.
背景情况:
- 卷积神经网络 (CNN) 在各种领域表现出色,包括蛋白质功能预测.
- 深度神经网络 (DNN) 面临数值稳定性挑战,影响可靠性和噪声敏感性.
- DeepGOPlus是一个最先进的CNN,用于在蛋白质组学中注释蛋白质序列.
研究的目的:
- 在推断阶段评估DeepGOPlus模型的数值稳定性.
- 探索使用低精度浮点格式的DeepGOPlus推理来优化内存和延迟.
- 量化浮点数据扰动引起的数值不确定性.
主要方法:
- 通过扰乱浮点数据来推断DeepGOPlus的量化数值不确定性.
- 使用蒙特卡罗算法来实验测量浮点运算错误.
- 使用VPREC工具以可定制的浮点精度模拟结果.
主要成果:
- 在其推理阶段,DeepGOPlus CNN表现出高数值稳定性.
- 使用精度较低的浮点格式进行选择性实现是可行的.
- 该模型有效地利用现有的浮点格式.
结论:
- 在数字上,DeepGOPlus的预测是可靠的.
- 为低精度格式优化DeepGOPlus需要仔细考虑.
- 该模型展示了当前浮点标准的高效使用.
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