self-training adaptation training domain

Training language models to follow instructions with human feedback

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ NeurIPS 2022 ......

instructions Training language feedback follow更新时间 2023-09-14

Proj. CRR Paper Reading: Optimal Speedup of Las Vegas Algorithms, Adaptive restart for stochastic synthesis

Title Adaptive restart for stochastic synthesis PLDI 2021 Task Distribute the power between multiple runs in stochastic program synthesis to accelerat ......

Algorithms stochastic synthesis Adaptive Optimal更新时间 2023-09-11

train the model model.fit

#train the model history = model.fit(x_train, y_train, batch_size=32, epochs=100, validation_split=0.1, shuffle=True, class_weight=class_weights, call ......

model train the fit更新时间 2023-09-11

mybatisplus中设置乐观锁，首先需要在表结构中添加一个字段表示乐观锁，之后再domain中对应的字段中添加上注解@Version，之后需要添加乐观锁的拦截器，然后在测试方法中进行测试

2023-09-10 version字段表示乐观锁在表结构中添加一个字段表示乐观锁 package com.hh.domain; import com.baomidou.mybatisplus.annotation.TableLogic; import com.baomidou.mybatispl ......

字段注解 mybatisplus Version 结构更新时间 2023-09-10

Implementing Domain Driven Design (Part Three)

# Example Use Cases This section will demonstrate some example use cases and discuss alternative scenarios. ## Entity Creation Creating an object from ......

Implementing Domain Design Driven Three更新时间 2023-09-09

Implementing Domain Driven Design (Part Two)

# Implementation: The Building Blocks This is the essential part of this guide. We will introduce and explain some **explicit rules** with examples. Y ......

Implementing Domain Design Driven Part更新时间 2023-09-08

Proj CDeepFuzz Paper Reading: SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

## Abstract 本文：SparseProp Github: https://github.com/IST-DASLab/sparseprop Task: a back-propagation algo for sparse training data, a fast vectorized i ......

Backpropagation SparseProp CDeepFuzz Efficient Networks更新时间 2023-09-08

论文解读（CST）《Cycle Self-Training for Domain Adaptation》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：Cycle Self-Training for Domain Adaptation论文作者：Hong Liu, Jianmin Wang, Mingsheng Long论文来源：2021 论文地址：down ......

Self-Training Adaptation Training Domain 论文更新时间 2023-09-07

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更新时间 2023-09-07

适配器模式(adapter)

# 适配器模式 ## 1 作用名字很形象的说出了模式的作用：当有一个需求需要Target的接口，然后有一个现成的Adaptee接口，为了让Adaptee接口匹配上Target接口，就需要使用Adapter，在Adapter中将Adaptee适配Target。 Adapter和Bridge模式都使用 ......

适配器 adapter 模式更新时间 2023-09-07

Proj CDeepFuzz Paper Reading: Natural attack for pre-trained models of code

## Abstract 背景：目前大多数的adversarial attack method on pre-trained models of code忽略了perturbations should be natural to human judges(naturalness requirement ......

pre-trained CDeepFuzz Natural Reading trained更新时间 2023-09-06

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更新时间 2023-09-06

结构型设计模式-适配器 Adapter

# 结构型设计模式-适配器 Adapter date: April 13, 2021 slug: design-pattern-adapter status: Published tags: 设计模式 type: Page ### 简介适配器模式是一种结构型设计模式，它能使接口不兼容的对象能够相 ......

结构型设计模式适配器 Adapter 结构更新时间 2023-09-06

论文精读：带有源标签自适应的半监督域适应（Semi-Supervised Domain Adaptation with Source Label Adaptation）

# Semi-Supervised Domain Adaptation with Source Label Adaptation 具有源标签适应的半监督域适应 >[原文链接](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Semi- ......

Adaptation Semi-Supervised Supervised 标签 Domain更新时间 2023-09-06

Xcode,swift:Error Domain=kCLErrorDomain Code=1 "(null)"问题解决

问题描述: iOS开发时,当使用用户的位置权限时,获取用户经纬度报错:Error Domain=kCLErrorDomain Code=1 "(null)",错误域=kCLError域代码=1“（null）” 解决方法: 打开模拟机的设置-通用-语言与地区将地区设置为中国(如果你的开发位置在中国的 ......

quot kCLErrorDomain Domain 问题 Xcode更新时间 2023-09-04

Implementing Domain Driven Design (A practical guide for implementing the Domain Driven Design with the ABP Framework)

##CONTENTS **Introduction**.................................................. * **Goal**...................................................... * **Sim ......

Domain Design Driven Implementing implementing更新时间 2023-09-02

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更新时间 2023-09-01

print ("标签为" + str(train_set_y[:, index]) + ", 这是一个'" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' 图片.")

这行代码使用 print 函数来输出一条信息。信息的内容是由多个字符串拼接而成的，其中包括 train_set_y 数组中指定索引处的值和 classes 数组中指定索引处的值。首先，"标签为" 是一个字符串字面量。接下来，str(train_set_y[:, index]) 表示获取 train ......

quot train_set_y index train set更新时间 2023-08-31

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self-training adaptation training domain

Training language models to follow instructions with human feedback

Proj. CRR Paper Reading: Optimal Speedup of Las Vegas Algorithms, Adaptive restart for stochastic synthesis

train the model model.fit

mybatisplus中设置乐观锁，首先需要在表结构中添加一个字段表示乐观锁，之后再domain中对应的字段中添加上注解@Version，之后需要添加乐观锁的拦截器，然后在测试方法中进行测试

Implementing Domain Driven Design (Part Three)

Implementing Domain Driven Design (Part Two)

Proj CDeepFuzz Paper Reading: SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

论文解读（CST）《Cycle Self-Training for Domain Adaptation》

Proj CDeepFuzz Paper Reading: PELICAN: Exploiting Backdoors of Naturally Trained Deep Learning Models In Binary Code Analysis

适配器模式(adapter)

Proj CDeepFuzz Paper Reading: Natural attack for pre-trained models of code

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

结构型设计模式-适配器 Adapter

论文精读：带有源标签自适应的半监督域适应（Semi-Supervised Domain Adaptation with Source Label Adaptation）

Xcode,swift:Error Domain=kCLErrorDomain Code=1 "(null)"问题解决

Implementing Domain Driven Design (A practical guide for implementing the Domain Driven Design with the ABP Framework)

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

print ("标签为" + str(train_set_y[:, index]) + ", 这是一个'" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' 图片.")

train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))

train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")

train_set_x_orig = np.array(train_dataset["train_set_x"][:])

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

Adapter 适配器模式简介与 C# 示例【结构型1】【设计模式来了_6】

论文解读（AdSPT）《Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis》

【五期邹昱夫】CCF-A（SP'23）3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Proj CDeepFuzz Paper Reading: An Extensive Study on Pre-trained Models for Program Understanding and Generation

日志开源组件（六）Adaptive Sampling 自适应采样

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

论文解读（PERL）《PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models》

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