526互联

surveillance detection learning maritime

Reward Modelling（RM）and Reinforcement Learning from Human Feedback（RLHF）for Large language models（LLM）技术初探

Reward Modelling（RM）and Reinforcement Learning from Human Feedback（RLHF）for Large language models（LLM）技术初探 ......
Reinforcement Modelling Learning Feedback language

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

[TOC] > [Qiu R., Huang Z., Ying H. and Wang Z. Contrastive learning for representation degeneration problem in sequential recommendation. WSDM, 2022.] ......
Representation Recommendation Degeneration Contrastive Sequential

[重读经典论文] RetinaNet——Focal Loss for Dense Object Detection

1. 前言这篇论文也是何凯明的团队在2017年的论文《Focal Loss for Dense Object Detection》中提出的，网络架构魔改了FPN，因此这篇论文的重点是提出了新的分类Loss——Focal Loss，用一个合适的函数，去度量难分类和易分类样本对总的损失函数的贡献。解决了 ......
mdash RetinaNet Detection amp 经典

Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning论文阅读笔记

## 摘要 连续学习过程中的稳定性-可塑性权衡是一个重要的问题。作者提出了Auxiliary Network Continual Learning (ANCL)，通过auxiliary network提高了模型的可塑性。 ## 方法 ### The Formulation of Auxiliary ......
Stability-Plasticity Plasticity Achieving Auxiliary Stability

火山引擎DataLeap的Catalog系统搜索实践（三）：Learning to rank与后续工作

Learning to rank Learning to rank主要分为数据收集，离线训练和在线预测三个部分。搜索系统是一个Data-driven system，因此火山引擎DataLeap的Catalog系统设计之初就需要考虑数据收集。收集的数据可以用来评估和提升搜索的效果。数据收集和在线预测前 ......
火山 DataLeap Learning Catalog 引擎

Reinforcement Learning之Q-Learning - Python实现

- **算法特征** ①. 以真实reward训练Q-function; ②. 从最大Q方向更新policy $\pi$ - **算法推导** **Part Ⅰ: RL之原理** 整体交互流程如下, 定义策略函数(policy)$\pi$, 输入为状态(state)$s$, 输出为动作(action ......
Learning Reinforcement Q-Learning Python

WebSocket-Learning-1

#### WebSocket ##### 1.什么是WebSocket >> WebSocket 是一种通讯协议，目标就是替代XmlHttpRequest 和长期轮询的解决方案。应用在实时弹幕、消息推送、棋牌游戏、聊天等需要及时通讯的场景。 ##### 2.WebSocket 连接有两个阶段 >>握 ......
WebSocket-Learning WebSocket Learning

强化学习基础篇[2]：SARSA、Q-learning算法简介、应用举例、优缺点分析

# 强化学习基础篇[2]：SARSA、Q-learning算法简介、应用举例、优缺点分析 # 1.SARSA SARSA（State-Action-Reward-State-Action）是一个学习马尔可夫决策过程策略的算法，通常应用于机器学习和强化学习学习领域中。它由Rummery 和 Niran ......
优缺点 算法 Q-learning learning 基础

Machine Learning 【note_02】

# note_02 Keywords: Classification, Logistic Regression, Overfitting, Regularization ## 1 Motivation Classification: - "binary classification": $y$ ca ......
Learning Machine note 02

Machine Learning 【note_01】

Declaration (2023/06/02): This note is the first note of a series of machine learning notes. At present, the main learning resource is *the 2022 Andre ......
Learning Machine note 01

Deep Isolation Forest for Anomaly Detection

# Deep Isolation Forest for Anomaly Detection ## 1 INTRODUCTION IForest的缺点 - 它的与坐标轴平行的隔离方法会导致它在高维/非线性空间中难以检测到异常。 如图1所示。红色为异常节点，蓝色为正常节点。红色被蓝色所包围，这种情况无法 ......
Isolation Detection Anomaly Forest Deep

翻译-Automatic detection of Long Method and God Class code smells through neural source code embeddings

# Automatic detection of Long Method and God Class code smells through neural source code embeddings 通过神经源代码嵌入自动检测 Long Method 和 God Class 代码异味 Aleksa ......
code embeddings Automatic detection through

论文阅读 | Learn from Others and Be Yourself in Heterogeneous Federated Learning

**在异构联邦学习中博采众长做自己** 代码：https://paperswithcode.com/paper/learn-from-others-and-be-yourself-in **摘要** 联邦学习中有异质性问题和灾难性遗忘。首先，由于非I.I.D（相同独立分布）数据和异构体系结构，模型在 ......
Heterogeneous Federated Learning Yourself Others

April 2023-Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation

本文基于深度q网络算法提出了记忆高效的强化学习算法来缓解这一问题。通过将目标q网络中的知识整合Knowledge Consolidation到当前q网络中，所提算法减少了遗忘并保持了较高的样本效率。 ......
Memory-efficient Consolidation Reinforcement Value-based Knowledge

零样本学习（Zero-shot Learning）

零样本学习是一种机器学习的问题设置，其中模型可以对从未在训练过程中见过的类别的样本进行分类，使用一些形式的辅助信息来关联已见和未见的类别。例如，一个模型可以根据动物的文本描述来识别动物，即使它从未见过那些动物的图像。 实现零样本学习有不同的方法，取决于辅助信息的类型和学习方法。以下是一些例子： 一种 ......
样本 Zero-shot Learning Zero shot

React使用redux报错：A non-serializable value was detected in an action...

原因：数据无法序列化，报错了 方法：在store.ts中，关闭序列化检测 middleware: (getDefaultMiddleware) => getDefaultMiddleware({ serializableCheck: false }) 有问题欢迎交流，谢谢！ ......
non-serializable serializable detected action React

事件抽取论文综述-A Survey on Deep Learning Event Extraction: Approaches and Applications

A Survey on Deep Learning Event Extraction: Approaches and Applications 1）发表信息： https://arxiv.org/abs/2107.02126 Qian Li, Jianxin Li, Member, IEEE, Ji ......
Applications Approaches Extraction Learning 事件

[论文速览] MAGE@MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

## Pre title: MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis accepted: CVPR2023 paper: https://arxiv.org/abs/221 ......
Representation Generative Synthesis Learning Encoder

End-to-End Object Detection with Transformers论文阅读笔记

## 摘要 作者提出了一种新的基于Transformer的目标检测模型DETR，将检测视为集合预测问题，无需进行nms以及anchor generation等操作。同时，对模型进行简单的修改就可以应用到全景分割任务中。 ## 方法 ### Object detection set predictio ......
Transformers End-to-End End Detection 笔记

Django - makemigrations - No changes detected

Django - makemigrations - No changes detected 回答1 To create initial migrations for an app, run makemigrations and specify the app name. The migrations ......
makemigrations detected changes Django No

[论文阅读] Few-shot Font Generation by Learning Style Difference and Similarity

## Pre title: Few-shot Font Generation by Learning Style Difference and Similarity accepted: Arxiv 2023 paper: https://arxiv.org/abs/2301.10008 code: ......
Difference Generation Similarity Few-shot Learning

[论文速览] RectifiedFlow@Flow Straight and Fast{colon}Learning to Generate and Transfer Data with Rectified Flow

## Pre title: Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow accepted: ICLR 2023 paper: https://arxiv.org/abs/2209 ......
Flow RectifiedFlow Rectified and Learning

2023CVPR_Learning a Simple Low-light Image Enhancer from Paired Low-light Instances(PairLLE)无监督

一. motivation 以前的大多数LIE算法使用单个输入图像和几个手工制作的先验来调整照明。然而，由于单幅图像信息有限，手工先验的适应性较差，这些解决方案往往无法揭示图像细节。 二. contribution 1. 提出一个成对低光图像输入（相同内容，不同的曝光度） 2. 在输入之前进行了一个 ......
Low-light light CVPR_Learning Instances Low

learn c++ 函数返回

......
函数 learn

The Difficulty of Passive Learning in Deep Reinforcement Learning

![](https://img2023.cnblogs.com/blog/1428973/202305/1428973-20230524224808789-13684847.png) **发表时间：**2021(NeurIPS 2021) **文章要点：**这篇文章提出一个tandem learni ......
Learning Reinforcement Difficulty Passive Deep

[HTML 5] Detect visualViewport change

On mobile device, when you open / close the keyboard, zoom in / out, it might affect the visual viewport view (the actual page content); to detect cha ......
visualViewport Detect change HTML

learn c++ 参数引用

#include <iostream> struct Role { int hp; int mp; int damage; }; bool Act(Role& Acter,Role& beAct) { beAct.hp -= Acter.damage; return beAct.hp < 0; } ......
参数 learn

Learning with Local and Global Consistency

[TOC] > [Zhou D., Bousquet O., Lal T. N., Weston J. and Scholk\ddot{o}pf B. Learning with local and global consistency. NIPS, 2003.](https://proceedin ......
Consistency Learning Global Local with

2023CVPR_Learning a Simple Low-light Image Enhancer from Paired Low-light Instances(PairLIE)

1、nn.ReflectionPad2d 对输入图像以最外围像素为对称轴，做四周的轴对称镜像填充。 大佬链接：(14条消息) torch.nn.ReflectionPad2d()的用法简介_nn.reflectionpad2d(1)_啊菜来了的博客-CSDN博客 # 对四周都填充3行 nn.Refl ......
Low-light light CVPR_Learning Instances Low

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers概述

0.前言 相关资料： arxiv github 论文解读 论文基本信息： 领域：弱监督语义分割 发表时间： CVPR 2022(2022.3.5) 1.针对的问题 目前主流的弱监督语义分割方法通常首先训练分类模型，基于类别激活图（CAM）或其变种生成初始伪标签；然后对伪标签进行细化作为监督信息训练一 ......
Weakly-Supervised Segmentation Transformers End-to-End Supervised
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