526互联

cross-domain sentiment analysis learning

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

Learn Git in 30 days——第 14 天： Git for Windows 选项设定

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 使用 Git for Windows 指令列工具绝对比通过 GUI 工具操作来的有效率，原因就在于你可以把许多重 ......
Git Windows Learn days for

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

Learn Git in 30 days——第 13 天：暂存工作目录与索引的变更状态

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 有没有遇过这种情境，某个系统开发写到一半，结果被老板或客戶「插单」，被要求紧急修正一个现有系统的 Bug 或添加 ......
索引 状态 目录 Learn days

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

A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

摘要 基于方面的情感分析（ABSA）由于其广泛的应用，近年来受到了越来越多的关注。在现有的ABSA数据集中，大多数句子只包含一个或多个具有相同情感极性的方面，这使得ABSA任务退化为句子级情感分析。在本文中，我们提出了一个新的大规模多方面多情感（MAMS）数据集，其中每个句子至少包含两个具有不同情感 ......
Aspect-Based Challenge Effective Sentiment Analysis

[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

Learn Git in 30 days——第 12 天：认识 Git 物件的相对名称

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 在认识了 Git 物件的「绝对名称」与「参照名称」后，最后我们来介绍 Git 版控过程中也很常用到的「相对名称」 ......
物件 Git 名称 Learn days

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

Learn Git in 30 days——第 11 天：认识 Git 物件的一般参照与符号参照

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 在认识了 Git 物件的「绝对名称」后，接下来就要介绍 Git 版控过程中最常用到的「参照名称」。 认识物件的参 ......
物件 Git 符号 Learn days

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

Proj CDeepFuzz Paper Reading: Combinatorial Testing for Deep Learning Systems

## Abstract 本文：DeepCT Task: Testing DL Models with Combinatorial Testing Method: 1. 将输出值的空间离散化为区间，以便覆盖每个区间,对不同层内的神经元交互进⾏采样，并减少必须执⾏的测试输⼊的数量。 2. a set o ......
Combinatorial CDeepFuzz Learning Reading Testing

机器学习 -> Machine Learning (III)

> 来做一些入门题吧. 以下大多是 kaggle 环境. **Q1 Titanic** https://www.kaggle.com/competitions/titanic import ``` # This Python 3 environment comes with many helpful ......
Learning 机器 Machine III gt

Meta-Learning, A Survey

## 一、概述 通常在机器学习里，我们需要用大量的数据来训练一个模型；当场景发生改变时，模型就需要重新训练。这显然提升了成本，而人类学习方式与此不同，一个小孩子在学习动物的过程中，学习了很多动物的名称，当某次给他看一些没有见过的动物时，他总能很快的将新动物和别的动物区分开。Meta learning ......
Meta-Learning Learning Survey Meta

aarch64/arm_v8 环境下编译Arcade-Learning-Environment —— ale-py

conda install g++=12 cmake ../ -DCMAKE_BUILD_TYPE=Release -DPYTHON_INCLUDE_DIR=/home/share/xxx/home/software/anaconda3/include -DPYTHON_LIBRARY=/home/ ......
下编 Arcade-Learning-Environment Environment Learning 环境

论文解读（SPGJL）《Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis论文作者：Jingli Shi、Weihua Li、Quan Bai ......
Cross-Domain Sentiment Analysis Learning Prompt

Q-learning and RL implementation

Aim: Train a model to properly play vintage video games... Deep Q-learning Algo~ Very short Brief of Notations: {A,pi(Policy),Q(quality of action-at a ......
implementation Q-learning learning and RL

Learn Git in 30 days——第 10 天：认识 Git 物件的绝对名称

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 在 Git 版本控制的过程，每一个版本就代表一个 commit 物件。又因为版控过程中经常会建立分支，最终产出的 ......
物件 Git 名称 Learn days

Proj CDeepFuzz Paper Reading: ACETest: Automated Constraint Extraction for Testing Deep Learning Operators

## Abstract Github: https://github.com/shijy16/ACETest 背景： 1. DL operators 用来计算多维tensors，很重要 本文：ACETest Task: automatically extract input validation c ......
Constraint Extraction CDeepFuzz Automated Operators

[论文阅读] Momentum contrast for unsupervised visual representation learning

# Momentum contrast for unsupervised visual representation learning ## Introduction 我们提出了动量对比（MoCo）作为一种构建具有对比损失的无监督学习的大型一致字典的方法（图1）。 我们将字典维护为数据样本队列：当前 ......
representation unsupervised Momentum contrast learning

Learn Git in 30 days——第 09 天：比对文件与版本差异

写的非常好的一个Git系列文章，强烈推荐 原文链接：https://github.com/doggy8088/Learn-Git-in-30-days/tree/master/zh-cn 使用任何版本控制软件的过程中，经常会需要查看历史记录与比对版本之间的差异。而在使用 Git 的时候要如何进行比对 ......
差异 版本 文件 Learn days

论文解读（WDGRL）《Wasserstein Distance Guided Representation Learning for Domain Adaptation》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Wasserstein Distance Guided Representation Learning for Domain Adaptation论文作者：Jian Shen、Yanru Qu、Weinan ......
Representation Wasserstein Adaptation Distance Learning
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