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ICCV2019_Slimmable:(US-Nets)Universally Slimmable Networks and Improved Training Techniques

Institute：University of Illinois at Urbana-Champaign Author：Jiahui Yu, Thomas Huang GitHub：https://github. com/JiahuiYu/slimmable_networks Introduction 　　最初的Slimmable networks基于预定义的width set切换网络宽度　　=> Motivation:can a single neural network

2022 年 9 月做题记录

2022 年 9 月做题记录 2022.8.28 Training Round #1 (1400-1700-1900-2000-2100-2200, 120min) A. CF888C K-Dominant Character

EVA

EVA 主要贡献构建2021年最大中文对话数据集WDC-Dialogue，有1.4B context-response pairs。基于Transformer架构，搭建2021年最大中文对话系统，有2.8B的参数量 WDC-Dialogue Dataset 数据收集 Repost 转发 Comment 评论 Q&A 问答数据清洗删除平台相关的tag信息，例如Reply to @*

GLIP_Grounded Language-Image Pre-training

一句话概括：多模态目标检测目录1、问题2、介绍和实现2.1 数据统一2.1.1 object detect数据转统一格式,补充prompt2.2.2 grounding数据转统一格式：自动生成box，怎么生成box？2.2 模型结构统一2.2.1 语言感知的融合2.3 loss 统一object detect loss转换3、效果4、分析和结论 1、问题 1、

BUPT 2022 Summer Training #6

题目链接：https://vjudge.net/contest/504104 D - It's a Mod, Mod, Mod, Mod World 是以前都没听说过的类欧几里得算法，呜 G - Intersecting Rectangles 题意给定n个矩形，判断是否存在两个矩形相交，即边框有交点，保证所有的横坐标互不相同，所有纵坐标互不相同。主席树做法把每个矩形

BUPT 2022 Summer Training #6

BUPT 2022 Summer Training #6(North American Invitational Programming Contest (NAIPC) 2019)

A B C D E F G H I J K L M 赛时过题 O O O 赛后补题

BUPT 2022 Summer Training #5

链接：https://vjudge.net/contest/504103#overview A - Berstagram 水题略 C - Trip to Saint Petersburg 题意有一堆事情，每件事情有开始时间和结束时间，以及做完这件事能赚到的钱。一个人来这个地方赚钱，他可以做任意多件事，且不同事件的时间可以重叠。但他每多待一天就得花k的伙食

BUPT 2022 Summer Training #2（2018-2019 ACM-ICPC, Asia Seoul Regional Contest）

E-LED 题目大意：已知N个点(vi,li)，求一个分段函数使得这些点在这个函数上的误差的最大值最小。数据范围：1≤N≤300000，0≤vi,li≤1e9; 解题思路：二分贪心。二分答案后尽量将排序后的数据归到L1，接着再放到L2，不能放了就是答案无效。这题的坑在于v=0这个点L0一定为0，还有L1≤L2，所以对于

A Recipe for Training Neural Networks-Andrej Karpathy

https://karpathy.github.io/2019/04/25/recipe/ 训练神经网络2个坑训练神经网络2个leaky abstraction 据说开始训练神经网络很容易。许多库和框架都觉得使用30行代码来解决数据问题很了不起，这给人一种即插即用的(错误的)印象。常见的做法是：在我们的脑子里，标准的软件就应该是

Baozi Training Leetcode solution 2320. Count Number of Ways to Place Houses

Problem Statement There is a street with n * 2 plots, where there are n plots on each side of the street. The plots on each side are numbered from 1 to n. On each plot, a house can be placed. Return the number of ways houses can be placed such th

CF1132D Stressful Training

题目链接题目见链接。题解方法一知识点：贪心，优先队列，二分。显然，这道题可以用二分答案做。check 函数可以用小根堆，让维持时间最小的先充电。但是不优化这道题会炸。有两个关键优化：一个是快读快写能省不少时间，还有一个是把维持天数当一个变量存起来以免重复运算浪费时间。其他

吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第三周测验

Practice quiz: Advice for applying machine learning 第 1 个问题：In the context of machine learning, what is a diagnostic? 【正确】A test that you run to gain insight into what is/isn’t working with a learning algorithm. An application of machine learning to m

吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第三

Practice quiz: Classification with logistic regression 第 1 个问题：Which is an example of a classification task? 【正确】Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not. Based on a patient's blood pressure, determine

Baozi Training Leetcode solution 2304. Minimum Path Cost in a Grid

Problem Statement You are given a 0-indexed m x n integer matrix grid consisting of distinct integers from 0 to m * n - 1. You can move in this matrix from a cell to any other cell in the next row. That is, if you are in cell (x, y) such th

论文解读（ARVGA）《Learning Graph Embedding with Adversarial Training Methods》

论文信息论文标题：Learning Graph Embedding with Adversarial Training Methods论文作者：Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang论文来源：2020, ICLR论文地址：download 论文代码：download 1 Introduction 　　众多图嵌入方法关注于保存图

training —— Applying Functional（函数式编程） Principles in C# 6 （overview）

Applying Functional Principles in C# 6 | Pluralsight c# 函数编程特性 linq & lambdas & delegates 函数编程：is mathematical function （!= class method） one value transforms to another value method signature ho

training —— Refactoring from Anemic Domain Model Towards a Rich One

Refactoring from Anemic Domain Model Towards a Rich One

核支持向量机

核支持向量机（SVM）是可以推广到更复杂模型的扩展，这些模型无法被输入空间的超平面定义。 SVM可以同时用于分类和回归 1、线性模型与非线性特征线性模型在低维空间中可能非常受限，因为线和平面的灵活性有限。有一种方法可以让线性模型变得更加灵活，就是添加更多的特征（添加输入特征的

Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection System

本文提出了一种更好的对测试集进行有效评估的方法，从而防止训练造成过拟合现象。经过实验表明，SVM和ANN对过拟合的免疫能力是最强的。链接为：https://arxiv.org/abs/2203.05232。 Cybersecurity has become one of the focuses of organisations. The number of cyberattacks ke

论文阅读《DETReg: Unsupervised Pre-training with Region Priors for Object Detection》

本文链接： https://arxiv.org/pdf/2106.04550.pdf 问题及创新点： 1.利用传统算法，选择一些可能存在物体目标的区域送入网络进行处理，作为伪标签（fbox）； 2.除了图像块伪标签，本文还采用其他预训练方法得到的基干网络来产生高维特征(femb)和块分类（fcat,是否是proposal块）作为伪标签上图

【CVPR 2019】论文阅读：3D human pose estimation in video with temporal convolutions and semi-supervised tr

2019 CVPR的文章，使用时序卷积和半监督训练的3D人体姿态估计论文链接：https://arxiv.org/abs/1811.11742 github：https://github.com/facebookresearch/VideoPose3D 已经有前辈对这篇文章做过理解：https://www.cnblogs.com/zeroonegame/p/15037269.html 此处不介绍引言和相关工作，具体

Image sizes for training and prediction

Image sizes for training and prediction Often, images that you use for training and inference have different heights and widths and different aspect ratios. That fact brings two challenges to a deep learning pipeline: PyTorch requires all images in a bat

WeChall CTF Writeup（一）

以下题目标题组成： [Score] [Title] [Author] 文章目录 0x01 1 Training: Get Sourced by Gizmore0x02 1 Training: Stegano I by Gizmore0x03 1 Training: Crypto - Caesar I by Gizmore0x04 1 Training: WWW-Robots by Gizmore0x05 1 Training: ASCII by Gizmore 0x01 1