recommendation heterogeneous preference 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 ......
A Contextualized Temporal Attention Mechanism for Sequential Recommendation
[TOC] > [Wu J., Cai R. and Wang H. D\'ej\`a vu: A contextualized temporal attention mechanism for sequential recommendation. WWW, 2020.](http://arxiv. ......
[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 ......
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」 ......
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
[TOC] > [Fan Z., Liu Z., Zhang J., Xiong Y., Zheng L. and Yu P. S. Continuous-time sequential recommendation with temporal graph collaborative transfo ......
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 ......
大模型时代的推荐系统Recommender Systems in the Era of Large Language Models (LLMs)
文章地址:https://arxiv.org/abs/2307.02046 笔记中的一些小实验中的模型都是基于GPT-3.5架构的ChatGPT模型。 本文主要讲述了比较具有代表性的方法利用LLM去学习user和item的表示,从预训练、微调和提示三个范式回顾了近期用于增强推荐系统的LLM先进技术, ......
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 ......
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 ......
[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==* ......
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 ......
Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
# Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [TOC] > [Huang L., Ma Y., Liu Y., Du B., Wang S. and Li ......
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 ......
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 ......
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
长尾问题是个老大难问题了。
在推荐中可以是用户/物料冷启动,在搜索中可以是中低频query、文档,在分类问题中可以是类别不均衡。长尾数据就像机器学习领域的一朵乌云,飘到哪哪里就阴暗一片。今天就介绍来自Google的一篇解决长尾物品推荐的论文。 ......
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/ ......
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 ......
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 ......
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 ......
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 ......
[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 ......
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 ......
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 ......
【五期邹昱夫】CCF-A(TIFS'23)SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability
> "Zhang, Zhuosheng, et al. "SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability." IEEE Transactions on Information For ......
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection (2)
Feature backbone采用DLA,输入维度为3×H×W的RGB图,得到维度D×h×w的特征图F,然后将特征图送入几个轻量级regression heads,2D bouding boxes的中心特征图用下面的模块得到: 其中AN是Attentive Normalization.用公式表示: ......
【五期邹昱夫】CCF-A(SP'23)3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning
> "Li, Haoyang, et al. "3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning." 2023 IEEE Symposium on Security an ......
Time-aware Path Reasoning on Knowledge Graph for Recommendation
[TOC] > [Zhao Y., Wang X., Chen J., Wang Y., Tang W., He X. and Xie H. Time-aware path reasoning on knowledge graph for recommendation. TOIS, 2022.](h ......
机器学习 -> Machine Learning (II)
> 这次来学习深度学习吧! # 1 训练前 ## 1.1 神经元与神经网络 神经元是神经网络的基本单位, 模拟了生物神经元的工作机制. 每个神经元接受一组输入, 将这些输入与其权重相乘, 然后对所有的乘积求和, 并加上一个偏置. 最后, 将得到的结果传递给激活函数. 神经网络由多个神经元组成, 这些 ......
jts learning
JTS简介 JTS提供了一套操作几何向量的java类库。早期版本 com.vividsolutions,已废弃不在维护。现在版本 com.locationtech.jts 由eclipse开源基金会托管 使用说明 入门指导 GIS开发入门指导 jts-core 核心库使用说明 jts-core核心类 ......
Proj CDeepFuzz Paper Reading: Deepxplore: Automated whitebox testing of deep learning systems
## Abstract 背景:现有的深度学习测试在很⼤程度上依赖于⼿动标记的数据,因此通常⽆法暴露罕⻅输⼊的错误⾏为。 本文:DeepXplore Task: a white-box framework to test DL Models 方法: 1. neuron coverage 2. diff ......