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meta—learning调研及MAML概述
背景 Meta Learning xff0c 又称为 learning to learn xff0c Meta Learning希望使得模型获取一种 学会学习 的能力 xff0c 使其可以在获取已有 知识 的基础上快速学习新的任务 xff0
Meta
Learning
MAML
论文阅读 MAML (Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks)
Model Agnostic Meta Learning for Fast Adaptation of Deep Networks MAML 论文阅读 摘要介绍模型不可知元学习元学习问题定义模型不可知元学习算法 MAML种类监督回归和分类强
MAML
model
Agnostic
Meta
Learning
A Simple Framework for Contrastive Learning of Visual Representations[论文学习] SimCLR
We simplify recently proposed contrastive self supervised learning algorithms without requiring specialized architecture
simple
Framework
for
Contrastive
Learning
CF 940F Machine Learning
传送门题目大意思路参考代码Remarks 传送门 题目大意 给你一个数组 a 1 n n 10 5 a 1 n
940F
Machine
Learning
人工智能导论(6)——机器学习(Machine Learning)
文章目录 一 概述二 重点内容三 思维导图四 重点知识笔记1 概述1 1 基本概念1 2 机器学习的分类 2 常见有监督学习算法2 1 线性回归2 2 多项式回归2 3 支持向量机2 4 k 最近邻分类2 5 朴素贝叶斯2 6 决策树2 7
Machine
Learning
人工智能导论
机器学习
【机器学习】MATLAB Deep Learning Toolbox输出Loss下降曲线
目录 MATLAB Deep Learning Toolbox输出Loss下降曲线 MATLAB Deep Learning Toolbox输出Loss下降曲线 MATLAB Deep Learning Toolbox可以通过训练选项 pl
MATLAB
Deep
Learning
Toolbox
loss
Dynamic Feature Learning for Partial Face Recognition (CVPR 2018)
破题 xff1a 本文提出的模型是Dynamic Feature Learning xff08 DFL xff09 本人要做的事情是Partial Face Recognition xff08 PFR xff09 摘要 xff1a DFL由
Dynamic
Feature
Learning
for
Partial
Deep-IRT Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory
写在前面 xff1a 本文在DKVMN的基础上结合项目IRT xff0c 加入了student ability network 和 difficulty network两个网络 xff0c 增加深度知识追踪的可解释性 1 摘要 基于深度学习
Deep
IRT
make
Learning
Based
AdamTechLouis's talk: Deep Learning with Knowledge Graphs
Last week I gave a talk at Connected Data London on the approach that we have developed at Octavian to use neural networ
AdamTechLouis
talk
Deep
Learning
with
python之数值运算程序_[Python Learning]数值计算
环境 TX Yun Ubuntu 14 04 1 LTS python 2 7 6 正文 加法运算 int 43 int gt int gt gt gt 1 43 1 2 int 43 float gt float gt gt gt 1 4
python
Learning
之数值运算程序
数值计算
(深度学习快速入门)Exploring Simple Siamese Representation Learning(SimSam)论文精读
文章目录 Abstract Introduction Related Work Method Emprical Study Stop gradient Predictor Batch Size Batch Normalization Sim
Exploring
simple
Siamese
Representation
Learning
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory
Deep IRT Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory Student Ability and Difficult
Deep
IRT
make
Learning
Based
English learning method ---我谈音标学习
最近在想这样一个问题 xff1a 每个地方都有自己的方言 xff0c 有些方言我们完全听不懂 xff0c 但是他们当地人却可以交流的很好 xff1b 而非本地人说方言 xff0c 就要模仿他们的腔调 xff0c 模仿的多了说出来的味也就像了
English
Learning
Method
我谈音标学习
Learning under Concept Drift:A Review
Learning under Concept Drift A Review Abstract Concept drift describes unforeseeable changes in the underlying distribut
Learning
under
Concept
Drift
review
【SLAM综述】A Survey on Deep Learning for Localization and Mapping
A Survey on Deep Learning for Localization and Mapping Towards the Age of Spatial Machine Intelligence
Slam
Survey
Deep
Learning
for
Federated Learning: 问题与优化算法
工作原因 xff0c 听到和使用Federated Learning框架很多 xff0c 但是对框架内的算法和架构了解不够细致 xff0c 特读论文以记之 这个系列计划要写的文章包括 xff1a Federated Learning 问题与
Federated
Learning
问题与优化算法
Machine Learning:k近邻算法(KNN)
目录 写在前面的话k 近邻算法概述优点缺点适用数据范围 原理Python代码实现Sklearn直接调用weights选项algorithm选项 算法测试与结果评价原理及方法函数主要参数说明Python代码实现 示例反思与总结 写在前面的话
Machine
Learning
KNN
近邻算法
【机翻】Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Contrastive Learning based Hybrid Networks for Long Tailed Image Classification Abstract 在长尾图像分类中 xff0c 判别式图像表示的学习起着非常重要
Contrastive
Learning
Based
Hybrid
networks
[Machine Learning & Algorithm] 随机森林(Random Forest)
1 什么是随机森林 xff1f 作为新兴起的 高度灵活的一种机器学习算法 xff0c 随机森林 xff08 Random Forest xff0c 简称RF xff09 拥有广泛的应用前景 xff0c 从市场营销到医疗保健保险 xff0c
Machine
Learning
amp
Algorithm
Random
联邦学习(Federated Learning)
联邦学习简介 联邦学习 xff08 Federated Learning xff09 是一种新兴的人工智能基础技术 xff0c 其设计目标是在保障大数据交换时的信息安全 保护终端数据和个人数据隐私 保证合法合规的前提下 xff0c 在多参与
Federated
Learning
联邦学习
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