概念定义和应用场景熵正则化和l1 l2 正则化类似,其使用形式都是在现有的loss function的基础上增加额外的一个正则损失项。 一图胜千言: 比较常见的使用方式是在模型的决策层的输出实施熵正则化,约束模型是输出…
In this chapter, we motivate the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly stated assumptions and can be applied to any smoothly parametrized model of posterior ...
"Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods." arXiv preprint arXiv:1912.05104 (2019). 特色 强化学习的主要问题是状态空间的探索,策略梯度方法要求在有一个较好的 restart distribution 的基础上才能够表现较好。 这篇文章介绍一种估计 discounted future state ...
网络熵正则化方法 网络释义 1. 熵正则化方法 向量熵正则... ... ) entropic regularization 熵正则化 )entropy regularization熵正则化方法) vector optimization 向量优化 ... www.dictall.com|基于2个网页
Entropy Regularization is a type of regularization used in reinforcement learning. For on-policy policy gradient based methods like A3C, the same mutual reinforcement behaviour leads to a highly-peaked $\pi\left(a\mid{s}\right)$ towards a few actions or
伪标签方法在半监督学习中，通过利用未标记数据与标记数据共同训练，提高模型泛化性能。其核心思想是将具有最大预测概率的类作为伪标签。这等同于熵最小化（Entropy Minimization）或熵正则化（Entropy Regularization），即通过减少未标记数据的预测不确定性，使决策边界更适应数据分布，从而减少类重叠，提高类...
Sparse Polynomial Chaos Expansion (PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviating the problem of dimensionality curse. However, current sparse PCE techniques focus on choosing f
consistently outperforms existing process reward models, achieving 1% improvement on GSM8K and 2-3% improvement on MATH under best-of-N evaluation, and more than 1% improvement under RLHF. These results highlight the efficacy of entropy-regularization in enhancing LLMs' reasoning capabilities. ...
Our suggested alternative is to add an entropy regularization to the KL divergence constraint, so as to build an improved TRPO with entropy regularization for KL divergence. We term this method as ERC-TRPO, which directly controls and adjusts the exploration of the agent using its constraint. Fu...
伪标签方法是一种同时从未标记数据和标记数据中学习的监督范式。将具有最大预测概率的类作为伪标签。形式化后等价于熵正则化(Entropy Regularization)或熵最小化(Entropy Minimization). 根据半监督学习的假设,决策边界应该尽可能通过数据较为稀疏的区域,即低密度区域,从而避免把密集的样本数据点分到决策边界的两侧,也...