▪ Cross Entropy => classification 如果使用cross entropy交叉熵损失函数这种作为loss,我们可以理解为是一个classification的问题。 Binary Classification 二分类 概率大于0.5预测为1,小于0.5预测为0。 如果损失函数是minimize MSE均方误差,我们可以理解为是regression问题
主要解决loss数值有大有小、学习速度有快有慢、更新方向时而相反的问题。最经典的两个工作有UWL(Uncertainty Weight):通过自动学习任务的uncertainty,给uncertainty大的任务小权重,uncertainty小的任务大权重;GradNorm:结合任务梯度的二范数和loss下降梯度,引入带权重的损失函数Gradient Loss,并通过梯度下降更新该权重。 也可...
本课程涉及深度学习和表示学习的最新技术,重点是有监督和无监督的深度学习、嵌入方法、度量学习、卷积网和递归网,并应用于计算机视觉、自然语言理解和语音识别。知识 校园学习 神经网络 人工智能 Yann Lecun 机器学习 深度学习 Python 卷积神经网络 pytorch安装 pytorch实战恭喜EDG夺得全球冠军赛总冠军! 评论9 最热 最新...
DropEdgefrom Ronget al.:DropEdge: Towards Deep Graph Convolutional Networks on Node Classification(ICLR 2020) DropNode,MaskFeatureandAddRandomEdgefrom Youet al.:Graph Contrastive Learning with Augmentations(NeurIPS 2020) DropPathfrom Liet al.:MaskGAE: Masked Graph Modeling Meets Graph Autoencoders(ar...
and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long...
Week 11 – Practicum- Prediction and Policy learning Under Uncertainty (PPUU) 1:23:19 Week 12 – Lecture- Deep Learning for Natural Language Processing (NLP) 1:40:57 Week 12 – Practicum- Attention and the Transformer 1:18:03 Week 13 – Lecture- Graph Convolutional Networks (GCNs) 2:00...
pythondata-sciencemachine-learningaitimeseriesdeep-learninggpupandaspytorchuncertaintyneural-networksforecastingtemporalartifical-intelligensetimeseries-forecastingpytorch-lightning UpdatedJun 1, 2025 Python Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam ...
ComputationPonderNet✨UncertaintyEvidential Deep Learning to Quantify Classification Uncertainty✨...
First is the field of uncertainty quantification, in which methods assess the model’s competence for a given sample [7]. Second – Out of Distribution Detection, where methods are intended to detect samples outside the model’s competence [8]. Third group of solutions is grouped in a field...
An ideal deep learning library should be easy to learn and use, flexible enough to be used in various applications, efficient so that we can deal with huge real-life datasets and accurate enough to provide correct results even in the presence of uncertainty in input data. Previous Next ...