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 ...
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...
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...关键词: Concept learning learning from examples noisy data probably approximately correct learning theoretical ...
Learning from noisy examples Mach. Learn. (1988) B.Frénayet al. Classification in the presence of label noise: a survey IEEE Trans. Neural Netw. Learn.Syst. (2013) J.Zhanget al. Learning saliency from single noisy labelling: arobust model fitting perspective ...
Even at the 50% noise level when all of the INCREMENTAL LEARNING FROMNOISY DATA 339 Figure5. STAGGER'S differential noise tolerance. positive instances are randomly designated as examples or nonexamples, STAGGER is still able to utilize the information present in the negative instances to form an...
CVPR2017: Learning from noisy large-scale datasets with minimal supervision 部分数据有噪声标签&精修标签,其他数据只有噪声标签 Network 共享CNN + 2个分类分支 Label Cleaning Network 作用:学习噪声标签到真实标签的映射 AE Loss:预测标签&真实标签的绝对误差 Image Classifier 作用:对图像进行分类 CE Loss:只在没...
There is a target to be learned, but it’s unknown to us. We have a set of examples generated by the target. The learning algorithm uses these examples to look for a hypothesis that approximates the target. 一个简单的学习模型 对于一个实际的学习问题来说, 期望的目标函数f和数据集X能通过...
Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. Here, we use aversive reinforcement to train
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-...
相反,Logic programming比如Inductive Logic Programming用很少数据训练后就能推理,但是Logic Programming对 噪声的加入 和 数据的错误标注 没有鲁棒性,更严重的是,Logic Programming不能应用到 数据歧义的 非符号 领域 比如 raw像素。 这篇文章提出Differentiable Inductive Logic framework可以有Logic Programming的特性(少量...