526互联

backpropagation sparseprop cdeepfuzz efficient

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

Proj CDeepFuzz Paper Reading: COMET: Coverage-guided Model Generation For Deep Learning Library Testing

## Abstract 背景：已有的方法(Muffin, Lemon, Cradle) can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. 本文：COMET Gi ......
Coverage-guided Generation CDeepFuzz Coverage 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 ......
Vulnerability Frameworks CDeepFuzz Automated Discovery

Proj CDeepFuzz Paper Reading: Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

## Abstract 本文： Task: 1. prove invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations 2. prove that ......
Invariance-inducing transformations regularization Invariance robustness

​MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression

​MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression MPDIoU:一个有效和准确的边界框损失回归函数 摘要 边界框回归(Bounding box regression, BBR)广泛应用于目标检测和实例分割，是目标定位 ......
Regression Efficient Accurate Bounding MPDIoU

Proj CDeepFuzz Paper Reading: Framework for Evaluating Faithfulness of Local Explanations

## Abstract 本文: Task: 1. study the faithfulness of an explanation system to the underlying prediction model on consistency and sufficiency 2. introduc ......
Faithfulness Explanations Evaluating CDeepFuzz Framework

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 ......
Inconsistencies Vulnerabilities Differential CDeepFuzz Framework

Proj CDeepFuzz Paper Reading: DeepTest: automated testing of deep-neural-network-driven autonomous cars

## Abstract 本文: DeepTest Task: a systematic testing tool for DNN-driven vehicles Method: 1. generated test cases with real-world changes like rain, fo ......
deep-neural-network-driven autonomous CDeepFuzz automated DeepTest

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 ......
multi-granularity granularity CDeepFuzz DeepGauge criteria

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 ......
Combinatorial CDeepFuzz Learning Reading Testing

Proj CDeepFuzz Paper Reading: Automatic differentiation in PyTorch

## Abstract 本文：描述automatic differentiation module of PyTorch 包括：Lua Torch, Chainer, HIPS Autograd Task: Provides a high-performance environment on dif ......
differentiation CDeepFuzz Automatic Reading PyTorch

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 ......
Constraint Extraction CDeepFuzz Automated Operators

Proj CDeepFuzz Paper Reading: AutoML: A survey of the state-of-the-art

## Abstract Github: https://github.com/marsggbo/automl_a_survey_of_state_of_the_art 本文： 1. intro AutoML methods: data preparation, feature engineering ......
state-of-the-art CDeepFuzz the Reading AutoML

Proj CDeepFuzz Paper Reading: Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation

## Abstract 背景： 1. the de facto standard to assess the quality of DNNs in the industry is to check their performance (accuracy) on a collected set of ......
Labeling-Free Estimation CDeepFuzz Efficient Accuracy

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

## Abstract ## 1. Intro ## 2. Background ### 2.1 Program Understanding and Generation Tasks ### 2.2 NL-PL Pre-Trained Models ![](https://img2023.cnblo ......
Understanding Pre-trained Generation CDeepFuzz Extensive

Proj CDeepFuzz Paper Reading: SyRust: automatic testing of Rust libraries with semantic-aware program synthesis

## Abstract 背景： 1. unsafe能够绕开rust type system 2. rust libraries中常有许多unsafe keyword 本文：SyRust Task: fuzz Rust library APIs Challenge: synthesize well-t ......
semantic-aware CDeepFuzz automatic libraries synthesis

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 ......
Deepxplore CDeepFuzz Automated whitebox learning

[论文精读][计算生物][蛋白质预训练表示]Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy

笔者正在调研市面上的蛋白表示方法，论文方法过于数理的部分会被抽象带过。 ## Basic Information: * Title: Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein St ......
Protein Data-Efficient 蛋白质 Pretraining Refinement

Efficient and Accurate Diagnostic Tool

Diagnostic tools play a crucial role in the automotive industry, allowing technicians to accurately identify and troubleshoot vehicle issues. Among th ......
Diagnostic Efficient Accurate Tool and

Classical Management: emphasized rationality and making organizations and workers as efficient as possible

Classical approach: First studies of management, which emphasized: * rationality * making organizations and workers as efficient as possible **Max Web ......
organizations rationality Management emphasized Classical

GLoRA：One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

# GLoRA：One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning ## O、Abstract 本文在 LoRA 的基础上，提出一种广义 LoRA （GLoRA，Generalized LoRA）。与 LoRA 相比，G ......
Parameter-Efficient Fine-tuning One-for-All Generalized Efficient

[VLDBJ 2022]Privacy and efficiency guaranteed social subgraph matching

# Privacy and efficiency guaranteed social subgraph matching ## 动机 目标是在不影响查询处理的同时保护隐私 ## 其中的子图匹配算法PGP ![img](https://img2023.cnblogs.com/blog/2988279/ ......
efficiency guaranteed matching subgraph Privacy

Efficient GPU-Accelerated Subgraph Matching

# Efficient GPU-Accelerated Subgraph Matching ## 总结 核心在利用GPU并行计算，为此设计了更适合GPU查询的数据结构，并混合BFS-DFS（先广度过滤再深度匹配）实现更好的时空复杂度 ## 动机 现有的算法都是先过滤再枚举。常规的CPU算法一次只能计 ......
GPU-Accelerated Accelerated Efficient Subgraph Matching

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

Efficient Correction of Single InsertionlDeletion and Multi-Substitution Errors

Efficient Correction of Single InsertionlDeletion and Multi-Substitution ErrorsG. J. Han, Y. L. Guan, K. Cai, K. S. Chan, and L. J. Kong A!JshYlc�A tw ......
InsertionlDeletion Multi-Substitution Substitution Correction Efficient

Efficient Graph Generation with Graph Recurrent Attention Networks

[TOC] > [Liao R., Li Y., Song Y., Wang S., Nash C., Hamilton W. L., Duvenaud D., Urtasun R. and Zemel R. NIPS, 2019.](http://arxiv.org/abs/1910.00760) ......
Graph Generation Efficient Attention Recurrent

Professional C++阅读笔记 chapter 29 Writing Efficient C++

# chapter 29 Writing Efficient C++ 1. 作者建议将所有class function包括析构函数 但是除了构造函数，都设为virtual的，因为virtual function的开销非常小 2. design 和 algrithm 比语言层面的优化重要太多 3. 在 ......
Professional Efficient chapter Writing 笔记

June 2021-Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp

本文建议通过对连续transition进行线性插值来合成新的transition用于训练。为了保持构建的transition的真实性，还开发了一个鉴别器来自动指导构建过程 ......
Continuous Efficiency Transition Improving Problems

May 2022-Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

提出了邻域混合经验回放(NMER)，一种基于几何的回放缓冲区，用状态-动作空间中最近邻的transition进行插值。NMER仅通过混合transition与邻近状态-动作特征来保持trnaistion流形的局部线性近似。 ......
Interpolation Neighborhood Experience Continuous Efficiency

Event Tables for Efficient Experience Replay

#Abstract 事件表分层抽样(SSET)，它将ER缓冲区划分为事件表，每个事件表捕获最优行为的重要子序列。 我们证明了一种优于传统单片缓冲方法的理论优势，并将SSET与现有的优先采样策略相结合，以进一步提高学习速度和稳定性。 在具有挑战性的MiniGrid域、基准RL环境和高保真赛车模拟器中的 ......
Experience Efficient Tables Replay Event
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