526互联

classification exploiting questions inference

论文阅读：A Lightweight Knowledge Graph Embedding Framework for Efficient Inference and Storage

ABSTRACT 现存的KGE方法无法适用于大规模的图（由于存储和推理效率的限制） 作者提出了一种LightKG框架： 自动的推断出码本codebooks和码字codewords，为每个实体生成合适的embedding。 同时，框架中包含残差模块来实现码本的多样性，并且包含连续函数来近似的实现码字的 ......
Lightweight Knowledge Embedding Efficient Framework

Chinese-Text-Classification-PyTorch

Chinese-Text-Classification Github项目地址： https://github.com/JackHCC/Chinese-Text-Classification-PyTorch 作者：JackHCC 链接：https://www.jianshu.com/p/9438fd0 ......
Chinese-Text-Classification-PyTor Classification Chinese PyTor Text

《ImageNet Classification with Deep Convolutional Neural Networks》阅读笔记

论文标题 《ImageNet Classification with Deep Convolutional Neural Networks》 ImageNet ：经典的划时代的数据集 Deep Convolutional：深度卷积在当时还处于比较少提及的地位，当时主导的是传统机器学习算法 作者 一作 ......
Classification Convolutional ImageNet Networks 笔记

WSL 炼丹报错：Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory

确认驱动没问题（nvidia-smi 可以正常使用） 解决办法参照：https://github.com/pytorch/pytorch/issues/85773#issuecomment-1288033297 内容如下： ......
libcudnn_cnn_infer file directory libcudnn library

Paper reading: Improving Deep Forest by Exploiting High-order Interactions

为了对深度森林设计出信息量更大、计算成本更低的特征表示，本文提出了一种新的深度森林模型——高阶交互深度森林(hiDF)，利用输入特征的稳定高阶交互来生成信息丰富且多样化的特征表示。具体而言，本文设计了一个广义版本的随机交叉树(gRIT)来发现稳定的高阶相互作用，并应用激活线性组合(ALC)将这些相互... ......
Interactions Exploiting High-order Improving reading

Paper Reading: Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems

针对欠采样方法会丢弃大量多数类样本导致信息缺失的问题，本文提出了基于哈希的欠采样集成 HUE 模型，它利用 Bagging 和多数类样本的分布特征来构建多样化的训练子集。首先 HUE 通过散列将大多数类样本划分为不同的特征子空间，然后使用所有少数样本和主要从同一哈希子空间中提取的部分多数样本来构建训... ......
Classification Hashing-Based Undersampling Imbalanced Ensemble

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: NeuRI: Diversifying DNN Generation via Inductive Rule Inference

## Abstract 背景：The correctness of DL systems is crucial for trust in DL applications 本文: NeuRI BaseTool: FreeFuzz Github: https://github.com/ise-uiuc/ ......
Diversifying Generation CDeepFuzz Inductive Inference

论文解读（MTEM）《Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classific ......
Meta-Tsallis-Entropy Classification Self-Training Minimization Adaptation

How to ask a good question on StackOverflow All In One

How to ask a good question on StackOverflow All In One 在 StackOverflow 上如何提出一个好问题 我们很乐意为你提供帮助，但为了提高你获得答案的机会，请遵循以下一些准则： ......
StackOverflow question good How All

论文解读（DEAL）《DEAL: An Unsupervised Domain Adaptive Framework for Graph-level Classification》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：DEAL: An Unsupervised Domain Adaptive Framework for Graph-level Classification论文作者：Nan Yin、Li Shen、Baop ......
DEAL Classification Unsupervised Graph-level Framework

论文解读（TAMEPT）《A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification》

论文信息 论文标题：A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification论文作者：Yunlong Feng, Bohan Li, Libo Qin, Xiao Xu, ......
Self-Supervised Classification Distillation Cross-Domain Supervised

论文解读（IW-Fit）《Better Fine-Tuning via Instance Weighting for Text Classification》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Better Fine-Tuning via Instance Weighting for Text Classification论文作者：论文来源：2021 ACL论文地址：download 论文代码：d ......
Classification Fine-Tuning Weighting Instance IW-Fit

[React Typescript] Inferring Type Arguments in Curried Hooks

import { DependencyList, useMemo, useState } from "react"; import { Equal, Expect } from "../helpers/type-utils"; const useCustomState = <TValue>(init ......
Typescript Inferring Arguments Curried React

Google classic interview questions, throwing eggs the least number of times All In One

Google classic interview questions, throwing eggs the least number of times All In One 谷歌经典面试题, 扔鸡蛋最少次数 你在一栋 100 层的大楼里工作，你得到 2 个相同的鸡蛋。 你需要计算出鸡蛋可以掉落到最高... ......
interview questions throwing classic Google

论文解读（CTDA）《Contrastive transformer based domain adaptation for multi-source cross-domain sentiment classification》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Contrastive transformer based domain adaptation for multi-source cross-domain sentiment classification论 ......
domain classification cross-domain multi-source Contrastive

论文解读（CBL）《CNN-Based Broad Learning for Cross-Domain Emotion Classification》

Note：[ wechat：Y466551 | 付费咨询，非诚勿扰 ] 论文信息 论文标题：CNN-Based Broad Learning for Cross-Domain Emotion Classification论文作者：Rong Zeng, Hongzhan Liu , Sancheng ......
Classification Cross-Domain CNN-Based Learning Emotion

[React Typescript] Fixing type inference in a Custom React Hook

// Problem import { useState } from "react"; import { Equal, Expect } from "../helpers/type-utils"; export const useId = (defaultId: string) => { cons ......
React Typescript inference Fixing Custom

使用swagger时出现Unable to infer base url. This is common when using dynamic servlet registra

在使用Swagger的时候访问地址后出现了错误,`http://localhost:8001/swagger-ui.html` 一直在弹窗提示,还取消不了 ![image-20230813164309945](https://img2023.cnblogs.com/blog/2446184/2023 ......
registra swagger dynamic servlet Unable

[React Typescript] Generic Inference through Multiple Type Helpers

import { Equal, Expect } from "../helpers/type-utils"; interface Button<T> { value: T; label: string; } interface ButtonGroupProps<T> { buttons: Butto ......
Typescript Inference Multiple Generic Helpers

Royal Questions题解

题目链接 Royal Questions - 洛谷 | 计算机科学教育新生态 (luogu.com.cn) 分析 每个公主会选择两个王子，考虑将每个公主所选择的两个王子连边，边权为该公主的嫁妆 选择该边即为选择该公主 那么结果图是什么呢？ 由于每个王子最多只能选择一个公主即每个点最多有1个出边（也可 ......
题解 Questions Royal

Paper Reading: FT4cip: A new functional tree for classification in class imbalance problems

本文提出了一种类不平衡问题的功能树(FT4cip)，该模型使用了考虑类不平衡的分割评估函数 Twoing，以及使用了一种优化 AUC 的新型剪枝算法。同时对多变量分割使用特征选择，进一步提高分类性能和可解释性。通过大量的实验分析证明，FT4cip 在 AUC 上的分类性能优于 LMT 和 Gama。... ......
classification functional imbalance problems Reading

[React Typescript] Ensure correct inference for prop types with satisfies & ComponentProps

import { ComponentProps } from "react"; import { Equal, Expect } from "../helpers/type-utils"; const buttonProps = { type: "button", // @ts-expect-err ......
ComponentProps Typescript inference satisfies correct

如何用Confusion matrix，classification report，ROC curve (AUC)分析一个二分类问题

ROC https://zhuanlan.zhihu.com/p/246444894 Sure, let's create a random confusion matrix as an example, and then I'll explain what each element in the ......
classification Confusion matrix report 问题

Paper Reading: A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty

受人类学习过程的启发，本文根据学习速度设计了样本难度模型，并提出了一种新的实例级再平衡策略。具体来说模型在每个训练周期记录每个实例的预测，并根据预测的变化来测量该样本的难度难度。然后对困难实例赋予更高的权重，对数据进行重新采样。本文从理论上证明了提出的重采样策略的正确性和收敛性，并进行一些实证实验来... ......
Class-Imbalanced Classification Re-Balancing Imbalanced Difficulty

CF875F Royal Questions题解

首先题目显然可以建模为一个二分图的最大权匹配问题。我们将王子放在左侧，公主放在右侧。根据贪心的思想，将公主按价值从大到小排序，每次搜索交错树；若找到未匹配节点，直接增广，否则丢弃该节点。这样我们就得到了一个 $O(m(m+n))$ 的算法。但这个复杂度显然不够优秀，我们要寻找加速它的方法。 首先直接 ......
题解 Questions Royal 875F 875

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

[TOC] > [Trivedi H., Balasubramanian N., Khot T., Sabharwal A. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-st ......
Knowledge-Intensive Chain-of-Thought Interleaving Multi-Step Intensive

Rethinking with Retrieval Faithful Large Language Model Inference

[TOC] > [He H., Zhang H. and Roth D. Rethinking with retrieval: faithful large language model inference. arXiv preprint arXiv:2301.00303, 2023.](http: ......
Rethinking Inference Retrieval Faithful Language

ModuleNotFoundError: No module named ‘tools.infer‘

导入paddleocr的时候报错ModuleNotFoundError: No module named 'tools.infer'，这里是由于python本来有个tools，和paddleocr内部的tools冲突，解决方法： 1. 找到paddleocr文件把所有导入tools.infer包的地 ......
ModuleNotFoundError module infer named tools

Paper Reading: Self-paced Ensemble for Highly Imbalanced Massive Data Classification

目前很多方法都不能很好地处理高度不平衡、大规模和有噪声的分类任务，主要原因是它们忽视了不平衡学习所隐含的困难。本文引入“分类硬度”的概念来刻画不平衡问题的困难所在，该概念表示为特定分类器正确分类样本的难度。基于这个概念，本文提出了一种新的学习框架——自定步速集成(self-pace Ensemble... ......
Classification Imbalanced Self-paced Ensemble Massive
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