因此(−AvgGradInner)是增加给定任务的不同minibatches间梯度内积的方向,可改善泛化能力 回想我们梯度表达式,我们能得到如下用于meta-gradients的表达式,使用的是k=2的SGD,三种算法的梯度期望为: 实际上,这三个梯度表达式首先都会将我们带到任务期望损失的最小值,然后更高阶的AvgGradInner项能通过最大化
on first-order meta-learning algorithms.on first-order meta-learning algorithms. Reptile是第一阶元学习算法的一种,它在MAML的基础上进行了一些改进,例如在计算梯度时,为了加速放弃了二阶求导,使用一阶微分近似进行代替。 Reptile的目标是实现相同分布的一类任务的少量样本快速学习。它的原理类似于First-Order MAML...
它还包括Reptile,这是我们在此处引入的新算法,该算法通过重复采样任务,对其进行训练并将初始化朝该任务的训练权重进行偏移。我们扩展了Finn et al.的结果,说明一阶元学习算法在一些公认的针对小样本分类的基准上表现良好,并且我们提供了旨在理解这些算法为何起作用的理论分析。 1 Introduction 2 Meta-Learning an Initi...
论文阅读笔记《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》 核心思想 本文是在MAML的基础上进一步探索利用元学习实现无模型限制的小样本学习算法。思路与MAML和Meta-LSTM比较接近,首先MAML是利用元学习的方式获得一个较好的初始化参数,在此基础上只需要进行少量样本的微调训练就可以得...
python -u run_miniimagenet.py --inner-batch 10 --inner-iters 8 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 15 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 15 --checkpoint ckpt_m55 # 1-shot 5-way Mini-ImageNet. python -u run_...
On first-order meta-learning algorithms (2018) arXiv:1803.02999 Google Scholar [23] Xiong N., Punnekkat S. Tiny federated learning with Bayesian classifiers IEEE 32nd Int. Symp. on Industrial Electronics (ISIE) (2023) Google Scholar [24] McMahan H.B., Moore E., Ramage D., y Arcas B...
learn2learnA PyTorch library that supports meta-RL. References [1] Finn, C., Abbeel, P. and Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. ICML 2017. [2] Nichol, A., Achiam, J. and Schulman, J. On first-order meta-learning algorithms. arXiv preprint...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
retrieval Distributed and parallel computing Graph algorithms Hierarchical memories Heuristics and meta-heuristics Mathematical programming Mobile computing Online algorithms Parameterized algorithms Pattern matching Quantum computing Randomized algorithms Scheduling and resource allocation problems Streaming algorithms ...
会议地点: Seoul, South Korea 届数: 3 浏览:7521关注:2参加:2 征稿 IEEE MetaCom 2025 provides a forum for academic researchers and industry practitioners to present research progress, exchange new ideas, and identify future directions in the field of Computing, Networking, and Applications for the...