fromexamplesprobablyapproximatelycorrectlearningnoisyThe basic question addressed in this paper is: how can a learning algorithm cope with incorrect training examples? Specifically, how can algorithms that produce an "approximately correct" identification with "high probability" for reliable data be adapted ...
Learning From Noisy Examples 喜欢 0 阅读量: 211 作者:D Angluin,P Laird 摘要: The basic question addressed in this paper is: how can a learning algorithm cope with incorrect training examples? Specifically, how can algorithms that produce an “approximately correct” identification with “high ...
Robust imitation learning from noisy demonstrations[J]. arXiv preprint arXiv:2010.10181, 2020. Motivation 模仿学习经常遇到因为轨迹质量良莠不齐而导致的鲁棒性问题,通常的解决方案需要引入额外的标注来解决,本文提出一种仅利用数据本身进行轨迹质量判别的逆向强化学习方案。 Assumptions 本文基于几个重要假设来进行...
Learning From Noisy Singly-labeled Data 下载积分: 500 内容提示: Published as a conference paper at ICLR 2018L EARNING F ROM N OISY S INGLY - LABELED D ATAAshish KhetanUniversity of Illinois at Urbana-ChampaignUrbana, IL 61801khetan2@illinois.eduZachary C. LiptonAmazon Web ServicesSeattle, WA...
Supervised learning depends on annotated examples, which are taken to be the \emph{ground truth}. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the cl...
2.4 《Peer Loss Functions Learning from Noisy Labels without Knowing Noise Rates》 This paper believe having the second feature above is non-trivial progress, and it features a promising solution to deploy in an unknown noisy traininghttp://environment.Inthis paper, we consider to establish a sco...
confusion: 假设存在多个标注者,regularizer使得估计的标签转移概率收敛到真实的标注者混淆矩阵;pre-training: fine-tuning比train from scratch泛化性好很多;loss-based gradient clipping(梯度裁剪的噪声鲁棒版);early-learning(参数分类分别拟合干净和噪声标签,只惩罚其中一类参数);random noiseto open-set examples。
BRACID: a comprehensive approach to learning rules from imbalanced data Among the problems related to the data distribution we focus on the role of small disjuncts, overlapping of classes and presence of noisy examples. Then... K Napierala,J Stefanowski - 《Journal of Intelligent Information Syst...
Regularized logistic regression differentiates OBS from EXP Observer behavior was reminiscent of a machine learning system that overfits the training data and generalizes poorly because it contains too many parameters and is trained on too few examples. In this sense, observers seemed to lack regulariza...
They can detect anomalies after learning from a single class of examples. Sohn et al. (2020) employed a two-stage framework for detecting anomalies using self-supervised learning models. In this framework, an SSL-based neural network is used to learn the representation of the input. A one-...