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Learning

元学习：元学习的始祖论文——《On the Optimization of a Synaptic Learning Rule》

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始祖 Optimization Learning Synaptic 论文

【流行前沿】DRAG Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data

今天再分享一篇9月的联邦学习领域处理异构数据分布的文章。看挂名是复旦的王昕，总的来说只能算是踏实的工作，但是新意上确实不太够。 文章的主要处理对象是解决异构数据在联邦训练中的client-drift问题，当然与很多相似论文一样，也将这个方法迁移到了拜占庭攻击的防范上。不过这个robustness仅通 ......
Divergence-based Aggregation Divergence Federated Adaptive

论文解读（CR-Match）《Revisiting Consistency Regularization for Semi-Supervised Learning》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Revisiting Consistency Regularization for Semi-Supervised Learning论文作者：Yue Fan、Anna Kukleva、Bernt Schie ......
Semi-Supervised Regularization Consistency Revisiting Supervised

Machine Learning for Beginners(scikit-learn module)

Machine Learning Common Lifycycle Import the Data Clean the Data Split the Data into Training/Test Sets Create a Model Train the Model Make Prediction ......
scikit-learn Beginners Learning Machine scikit

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

目录概符号说明Ranking Distillation代码 Tang J. and Wang K. Ranking Distillation: Learning compact ranking models with high performance for recommender system. ......
Ranking Distillation Performance Recommender Learning

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5331-5340, 2019 ......
Meta-Reinforcement Reinforcement Probabilistic Off-Policy Efficient

MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses

1. 论文简介 论文题目：MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses Paper地址：chrome-extension://efaidnbmnnnibpcajpcglclefind ......
Generalizable Monocular MonoNeRF Learning without

Meta-Reinforcement Learning of Structured Exploration Strategies

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ NeurIPS 2018 ......
Meta-Reinforcement Reinforcement Exploration Structured Strategies

Varibad：A very good method for bayes-adaptive deep rl via meta-learning

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ Published as a conference paper at ICLR 2020 ABSTRACT 1 INTRODUCTION 2 BACKGROUND 2.1 TRAINING SETUP 2.2 BAYESIAN REINF ......
bayes-adaptive meta-learning adaptive learning Varibad

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

目录概符号说明TimelyRecMulti-aspect Time Encoder (MATE)Time-aware History Encoder (TAHE)Prediction代码 Cho J., Hyun D., Kang S. and Yu H. Learning heterogeneou ......
Recommendation Heterogeneous Preference Learning Temporal

论文解读（FixMatch）《FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence论文作者：论文来源：2020 aRxiv论文地址： ......
FixMatch Semi-Supervised Consistency Simplifying Confidence

Learning Continuous Image Representation with Local Implicit Image Function

Learning Continuous Image Representation with Local Implicit Image Function（阅读笔记）11.03 局部隐式图像函数（LIIF）表示连续中的图像，可以以任意高分辨率表示。 摘要：如何表示图像？当视觉世界以连续的方式呈现时，机器 ......
Image Representation Continuous Learning Function

Unsupervised Degradation Representation Learning f

Unsupervised Degradation Representation Learning for Blind Super-Resolution文献阅读 (2022.09.28)盲超分辨率的退化表征（向量）学习 摘要：大多数基于CNN的SR都是基于退化固定且可知这一假设。但是实际退化和假设不一 ......
Representation Unsupervised Degradation Learning

SpringBoot-Learning系列之Kafka整合

SpringBoot-Learning系列之Kafka整合 本系列是一个独立的SpringBoot学习系列，本着 What Why How 的思想去整合Java开发领域各种组件。 消息系统 主要应用场景 流量消峰(秒杀 抢购)、应用解耦（核心业务与非核心业务之间的解耦） 异步处理、顺序处理 实时数据 ......
SpringBoot-Learning SpringBoot Learning Kafka

Graph Construction and b-Matching for Semi-Supervised Learning

目录概符号说明图的构建Graph Sparsification\(\epsilon\)-neighborhood graph\(k\)NN graph\(b\)-MatchingGraph Edge Re-Weighting Jebara T., Wang J. and Chang S. Graph ......
Semi-Supervised Construction Supervised b-Matching Matching

论文解读（LR2E）《Learning to Reweight Examples for Robust Deep Learning》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Learning to Reweight Examples for Robust Deep Learning论文作者：Mengye Ren、Wenyuan Zeng、Bin Yang、Raquel Urta ......
Learning Examples Reweight Robust 论文

Proj CDeepFuzz Paper Reading: Metamorphic Testing of Deep Learning Compilers

## Abstract 背景：Compiling DNN models into high-efficiency executables is not easy: the compilation procedure often involves converting high-level model ......
Metamorphic CDeepFuzz Compilers Learning Reading

Machine learning note(1)

注：本笔记不给出完整解释 ## 正规方程 设$z=\theta^{T}x$ 设损失函数为$J(\theta)$，求令$\frac{\partial J}{\partial \theta}=0$的$\theta$ 由此得出最优的$\theta$ ## 牛顿迭代 回顾一下梯度下降：$\theta'=\t ......
learning Machine note

Proj CDeepFuzz Paper Reading: A Comprehensive Study of Deep Learning Compiler Bugs

## Abstract 背景：深度学习编译器处理的深度学习模型与命令式程序有根本的不同，因为深度学习模型中的程序逻辑是隐式的。（the DL models processed by DL compilers differ fundamentally from imperative programs ......
Comprehensive CDeepFuzz Compiler Learning Reading

Proj CDeepFuzz Paper Reading: DeepMutation: Mutation Testing of Deep Learning Systems

## Abstract 本文：DeepMutation Github: https://github.com/berkuva/mutation-testing-for-DNNs Task: mutation testing framework specialized for DL systems t ......
DeepMutation CDeepFuzz Mutation Learning Reading

Proj CDeepFuzz Paper Reading: TensorFlow: a system for Large-Scale machine learning

## Abstract 本文:Tensorflow Github: https://github.com/tensorflow/tensorflow Task: Detail on Tensorflow dataflow model 特点: 1. operates at large scale an ......
Large-Scale TensorFlow CDeepFuzz learning Reading

Proj CDeepFuzz Paper Reading: PyTorch: an imperative style, high-performance deep learning library

## Abstract 本文： PyTorch Task: detail the implementation and architecture of PyTorch Github: https://github.com/pytorch/pytorch 特点： 1. PyTorch同时关注可用性和速 ......
high-performance performance imperative CDeepFuzz learning

Proj CDeepFuzz Paper Reading: PELICAN: Exploiting Backdoors of Naturally Trained Deep Learning Models In Binary Code Analysis

## Abstract 背景： 1. 本文研究的不是被恶意植入的后门，而是products of defects in training 2. 攻击模式: injecting some small fixed input pattern(backdoor) to induce misclassifi ......
Exploiting CDeepFuzz Backdoors Naturally Analysis

Proj CDeepFuzz Paper Reading: Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests

## Abstract 背景：In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning mod ......
Non-Deterministic Effectiveness Deterministic CDeepFuzz Balancing

Proj CDeepFuzz Paper Reading: COMET: Coverage-guided Model Generation For Deep Learning Library Testing

## Abstract 背景：已有的方法(Muffin, Lemon, Cradle) can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. 本文：COMET Gi ......
Coverage-guided Generation CDeepFuzz Coverage Learning

Proj CDeepFuzz Paper Reading: IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks

## Abstract 本文：IvySyn Task: discover memory error vulnerabilities in DL frameworks BugType: memory safety errors, fatal runtime errors Method: 1. 利用na ......
Vulnerability Frameworks CDeepFuzz Automated Discovery

[论文阅读] Learning Semi-supervised Gaussian Mixture Model

# Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery ## Abstract 在本文中，我们解决了广义类别发现(generalized category discovery, GCD ......
Semi-supervised supervised Learning Gaussian Mixture

Proj CDeepFuzz Paper Reading: Differential Testing of Cross Deep Learning Framework APIs: Revealing Inconsistencies and Vulnerabilities

## Abstract 背景：目前对cross-framework conversion中的inconsistencies和security bugs的研究少有 本文：TensorScope Task: test cross-frame APIs in Machine Learning Librar ......
Inconsistencies Vulnerabilities Differential CDeepFuzz Framework

Proj CDeepFuzz Paper Reading: DeepGauge: multi-granularity testing criteria for deep learning systems

## Abstract 本文: DeepGauge Task: provide multi-granularity testing criteria for DL systems Method: multi-granularity testing criteria for DL systems: 1 ......
multi-granularity granularity CDeepFuzz DeepGauge criteria

[论文阅读] Prototypical contrastive learning of unsupervis

# Prototypical contrastive learning of unsupervised representations ## abstract 这篇论文介绍了原型对比学习（PCL），一种将对比学习与聚类相结合的无监督表示学习方法。PCL不仅为实例区分任务学习低层特征，更重要的是==* ......
Prototypical contrastive unsupervis learning 论文
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