meta learning 初始化embedding

2024-10-03

《Learning to warm up cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks》论文阅读

<Learning to warm up cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks>论文阅读 (i)问题背景: 工业界的推荐系统/广告系统现在都会用embedding技术生成物品/用户的向量.通俗点讲就是build一个向量嵌入层,把带有原始特征的输入向量转换成一个低维度的dense向量表示.推荐系统的模型一般有向量嵌入层和深度模型层两部分组成,向量嵌入层的

论文笔记：Visual Question Answering as a Meta Learning Task

Visual Question Answering as a Meta Learning Task ECCV 2018 2018-09-13 19:58:08 Paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/Damien_Teney_Visual_Question_Answering_ECCV_2018_paper.pdf 1. Introduction: 本文提出一种新的 VQA 思路,将 meta-learning 结

[转载]Meta Learning单排小教学

原文链接:Meta Learning单排小教学虽然Meta Learning现在已经非常火了,但是还有很多小伙伴对于Meta Learning不是特别理解.考虑到我的这个AI游乐场将充斥着Meta Learning的分析解读及各种原创思考,所以今天Flood就和大家做一个Meta Learning小教学,希望能够用最简短,最通俗的语言来让大家明白Meta Learning的概念,Meta Learning的几种研究方法,以及Meta Learning未来的发展,带大家上分!相信这个Meta L

深度学习课程笔记（十七）Meta-learning (Model Agnostic Meta Learning)

深度学习课程笔记(十七)Meta-learning (Model Agnostic Meta Learning) 2018-08-09 12:21:33 The video tutorial can be found from: Model Agnostic Meta Learning Related Videos: My talk for Model Agnostic Meta Learning with domain adaptation Paper: https://arxiv.org/p

（转）Paper list of Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning

Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning 2018-08-03 19:16:56 本文转自:https://github.com/floodsung/Meta-Learning-Papers 1 Legacy Papers [1] Nicolas Schweighofer and Kenji Doya. Meta-learning in reinforcement learning. Neural

论文阅读之：Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space

Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space 2018-01-04 15:58:15 写在前面:为什么要看这个paper?这篇 paper 貌似是第一个将 meta-learning 应用到 visual tracking 领域的,取得了速度和精度较好的平衡. Introduction: 我们知道,tracking 中比较重要的就是 target object

The Rise of Meta Learning

The Rise of Meta Learning 2019-10-18 06:48:37 This blog is from: https://towardsdatascience.com/the-rise-of-meta-learning-9c61ffac8564 Connor Shorten Follow Oct 16 · 9 min read https://openai.com/blog/solving-rubiks-cube/ Meta-Learning describes th

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning 2019-08-11 19:48:17 Paper: https://arxiv.org/pdf/1903.10258.pdf Code: https://github.com/liuzechun/MetaPruning 1. Background and Motivation:

Learning Combinatorial Embedding Networks for Deep Graph Matching（基于图嵌入的深度图匹配）

1. 文献信息题目: Learning Combinatorial Embedding Networks for Deep Graph Matching(基于图嵌入的深度图匹配) 作者:上海交通大学研究团队(Runzhong Wang ,Junchi Yan,Xiaokang Yang) 期刊:ICCV 2019 注:此篇论文篇幅较长,其中涉及图匹配等问题,为方便阅读,保留了较多关键信息. 2. 背景这篇论文聚焦于计算机视觉领域一项历久弥新的问题:图匹配问题.在计算机视觉中,图匹配旨在利用图

【MetaPruning】2019-ICCV-MetaPruning Meta Learning for Automatic Neural Network Channel Pruning-论文阅读

MetaPruning 2019-ICCV-MetaPruning Meta Learning for Automatic Neural Network Channel Pruning Zechun Liu (HKUST).Xiangyu Zhang (MEGVII).Jian Sun(MEGVII) GitHub:251 stars Citation:20 Motivation A typical pruning approach contains three stages: training

论文解读（ARVGA）《Learning Graph Embedding with Adversarial Training Methods》

论文信息论文标题:Learning Graph Embedding with Adversarial Training Methods论文作者:Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang论文来源:2020, ICLR论文地址:download 论文代码:download 1 Introduction 众多图嵌入方法关注于保存图结构或最小化重构损失,忽略了隐表示的嵌入分布形式,因此本文提出对

【元学习】Meta Learning 介绍

目录元学习(Meta-learning) 元学习被用在了哪些地方? Few-Shot Learning(小样本学习) 最近的元学习方法如何工作 Model-Agnostic Meta-Learning (MAML) 元学习(Meta-learning) 智能的一个关键方面是多功能性--做许多不同事情的能力.当前的AI系统可以做到精通于某一项技能,但是,如果我们要求AI系统执行各种看似简单的问题(用同一个模型去解决不同问题),它将会变得十分困难.相反,人类可以明智地利用以往经验并采取行动以适应各

什么是 Meta Learning / Learning to Learn ?

Learning to Learn Chelsea Finn Jul 18, 2017 A key aspect of intelligence is versatility – the capability of doing many different things. Current AI systems excel at mastering a single skill, such as Go, Jeopardy, or even helicopter aerobatics. But

14-2-Unsupervised Learning ----Word Embedding

Introduction 词嵌入(word embedding)是降维算法(Dimension Reduction)的典型应用那如何用vector来表示一个word呢? 1-of-N Encoding 最传统的做法是1-of-N Encoding,假设这个vector的维数就等于世界上所有单词的数目,那么对每一个单词来说,只需要某一维为1,其余都是0即可:但这会导致任意两个vector都是不一样的,你无法建立起同类word之间的联系 Word Class 还可以把有同样性质的word进行聚类(

网络表示学习Network Representation Learning/Embedding

网络表示学习相关资料网络表示学习(network representation learning,NRL),也被称为图嵌入方法(graph embedding method,GEM)是这两年兴起的工作,目前很热,许多直接研究网络表示学习的工作和同时优化网络表示+下游任务的工作正在进行中. 清华大学计算机系的一个学习组新浪微博@涂存超整理的论文列表:https://github.com/thunlp/NRLpapers,并一直持续更新着,里面详细的列举了最近几年有关网络表示学习(networ

[论文分享] DHP: Differentiable Meta Pruning via HyperNetworks

[论文分享] DHP: Differentiable Meta Pruning via HyperNetworks authors: Yawei Li1, Shuhang Gu, etc. comments: ECCV2020 cite: [2003.13683] DHP: Differentiable Meta Pruning via HyperNetworks (arxiv.org) code: ofsoundof/dhp: This is the official implementati

Deep Learning for Information Retrieval

最近关注了一些Deep Learning在Information Retrieval领域的应用,得益于Deep Model在对文本的表达上展现的优势(比如RNN和CNN),我相信在IR的领域引入Deep Model也会取得很好的效果. IR的范围可能会很广,比如传统的Search Engine(query retrieves documents),Recommendation System(user retrieves items)或者Retrieval based Question Answe

【CV】ICCV2015_Unsupervised Visual Representation Learning by Context Prediction

Unsupervised Visual Representation Learning by Context Prediction Note here: it's a learning note on unsupervised learning model from Prof. Gupta's group. Link: http://120.52.73.9/www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsu

论文阅读笔记 Improved Word Representation Learning with Sememes

论文阅读笔记 Improved Word Representation Learning with Sememes 一句话概括本文工作使用词汇资源--知网--来提升词嵌入的表征能力,并提出了三种基于知网资源的词嵌入学习模型,在通用的中文词嵌入评测数据集上进行了评测,取得了较好的结果. 作者简介该论文选自 ACL 2017,是清华大学孙茂松刘知远老师组的成果.论文的两名共同第一作者分别是牛艺霖和谢若冰. 牛艺霖,清华本科生. 谢若冰,清华研究生(2014-2017),清华本科生(2010-20