Learning to Learn with Gradients(一) 《Learning to Learn with Gradients》是 MAML 作者 Chelsea B. Finn 的博士论文,详细的讲了 MAML 相关的想法、推导、扩展和应用,非常值得一读。 A tree that is unbending, is easily broken. — Lao Tzu 论文的开篇,就引用了老子的一句话 “曲则全,枉则直(A tree t...
例如语音识别,文本翻译,图像中的分类任务,玩雅达利游戏以及拧上瓶盖(原文是“playing Atari breakout or screwing on a bottle cap”,我的理解就是打游戏和机械操作任务,强化学习相关)。随后针对这些任务,将训练一个模型或策略,例如使用端到端的方式等,从一个随机开始的状态起始进行模型训练,并解决这个问题。在机器...
The use of recurrence allows the LSTM to learn dynamic update rules which integrate informa- Figure 3: One step of an LSTM optimizer. All tion from the history of gradients, similar to LSTMs have shared parameters, but separate hid- momentum. This is known to have many desir- den states....
We then computed the loss L between the predicted mask and the ground truth mask in the support set with L2 -regularization. Validation loss on the query data completes the task for which the optimized φi is fed to the meta-learner where meta-gradients are analytically computed and updated ...
基于策略(Policy-Based):直接输出下一步动作的概率,根据概率来选取动作。但不一定概率最高就会选择该动作,还是会从整体进行考虑。适用于非连续和连续的动作。常见的方法有Policy gradients。 基于价值(Value-Based):输出的是动作的价值,选择价值最高的动作。适用于非连续的动作。常见的方法有Q-learning、Deep Q Netwo...
Thus the algorithmic novelty is minor and amounts to combining two techniques to address a different problem statement. though meta learning could be a solution to learn with few examples, the solution being used in this work is not meta learning and so should not be in the title to avoid ...
So, what is learning to learn, and what has it been used for? Early approaches to meta-learning date back to the late 1980s and early 1990s, includingJürgen Schmidhuber’s thesisandwork by Yoshua and Samy Bengio. Recently meta-learning has become a hot topic, with a flurry of recent pa...
When using Gradient Descent, you should ensure that all features have a similar scale (e.g., using Scikit-Learn’sStandardScalerclass), orelse it will take much longer to converge. This diagram also illustrates the fact that training a model means searching for a combination of model parameters...
We aim to find a well-initialized 𝜃θ that will lead to a good adaptation of 𝜃θ on the unseen new task 𝒯𝑖Ti after a few gradient descent steps. As a representative optimization meta-learning method, MAML [3] leverages few-shot samples to learn new tasks. The primary objective...
Since the ability to ‘learn’ is considered a sign of intelligence, machine learning is hence a part of artificial intelligence. And deep learning is a subset of machine learning. It has the same goal as machine learning (to make computers learn) but approaches the problem with neural network...