embeddingtaskmetaachillelearningiccv TASK2VEC:TaskEmbeddingforMeta-LearningAlessandroAchille1,2MichaelLam1RahulTewari1AvinashRavichandran1SubhransuMaji1,3CharlessFowlkes1,4StefanoSoatto1,2PietroPerona1,5achille@cs.ucla.edu{michlam,tewarir,ravinash,smmaji,fowlkec,soattos,peronapp}@amazon1AWS2UniversityofCali...
《Task2Vec: Task Embedding for Meta-Learning》A Achille, M Lam, R Tewari, A Ravichandran, S Maji, C Fowlkes, S Soatto, P Perona [UCLA & AWS & UMass & UCI] (2019) http://t.cn/EcB4aYV view:http://t.c...
Given a dataset with ground-truth labels and a loss function, we process images through a "probe network" and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is ...
This is an implementation of the Task2Vec method described in the paperTask2Vec: Task Embedding for Meta-Learning. Task2Vec provides vectorial representations of learning tasks (datasets) which can be used to reason about the nature of those tasks and their relations. In particular, it provides...
引言Word2Vec是google提出的一个学习word vecor(也叫word embedding)的框架。 它主要提出了两个模型结构CBOW和Skip-gram,这两个模型都属于Log Linear模型,结构如下所示: CBOW对小型数据比较合适,而Skip-gram在大型语料中表现得更好。 CBOW模型 CBOW main idea:Predict center word from (b... ...