526互联

labeling-free estimation cdeepfuzz efficient

SHARPNESS-AWARE MINIMIZATION FOR EFFICIENTLY IMPROVING GENERALIZATION论文阅读笔记

Intro 在训练集上最小化损失很可能导致泛化性低，因为当今模型的过参数化会导致training loss的landscape异常复杂且非凸，包含很多local/global minima，因此优化器的选择至关重要。loss landscape的几何性质（特别是minima的flatness）与泛化 ......
SHARPNESS-AWARE GENERALIZATION MINIMIZATION EFFICIENTLY SHARPNESS

LocPatcH An efficient long-read hybrid error correction algorithm based on local pHMM

该文档主要介绍了一种基于装配的方法和概率隐藏马尔科夫模型 (pHMM) 用于纠正长读序列的错误。文档详细描述了对酵母数据进行实验的结果、纠正方法的拓扑结构以及实验设置和数据集。 这种基于装配的纠正方法相对于直接纠正存在哪些优势？ pHMM 的拓扑结构是怎样的？ 在实验中使用了什么样的数据集？ 提示： ......
correction efficient algorithm long-read LocPatcH

Bias of an estimator

Bias of an estimator Difference between an estimator's expected value from a parameter's true value For broader coverage of this topic, see Bias (stat ......
estimator Bias of an

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery * Authors: [[Libo Wang]], [[Rui Li]], [[ ......
segmentation transformer UNetFormer UNet-like efficient

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network * Authors: [[Wenzhe Shi]], [[Jose Caballer ......
Super-Resolution Convolutional Resolution Real-Time Efficient

High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

目录简介创新点内容Entropy Coding Using Multistage Context Model模型结构残差邻域注意力块Residual Neighborhood Attention Block RNAB激活函数 高斯误差线性单元激活函数GELU并行解码 简介 创新点 Integrate ......
High-Efficiency Neighborhood Aggregation Information Efficiency

【HFSS】仿真时radiation efficiency（辐射效率）大于1

![image](https://img2023.cnblogs.com/blog/2603751/202311/2603751-20231121124817927-404911195.png) ![image](https://img2023.cnblogs.com/blog/2603751/20... ......
efficiency radiation 效率 HFSS

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

目录概TallRec代码 Bao K., Zhang J., Zhang Y., Wang W., Feng F. and He X. TALLRec: An effective and efficient tuning framework to align large language model ......
Recommendation Effective Efficient Framework Language

2023CVPR_Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

一. Motivation 1. Transformer在解决全局表现很好，但是复杂度很高，主要体现在QK的乘积: (We note that the scaled dot-product attention computation is actually to estimate the corre ......
CVPR_Efficient Domain-based Transformers High-Quality Deblurring

Checkerboard Context Model for Efficient Learned Image Compression

目录AbstractIntroductionPreliminary 初步介绍Variational Image Compression with Hyperprior(超先验变分图像压缩)Autoregressive Context(自回归上下文模型)Parallel Context Modelin ......
Checkerboard Compression Efficient Context Learned

ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding

abstruct \(\quad\) 受能量压缩表现的启发，提出了不均匀通道情况自适应编码.结合不均匀分组模型和现有上下文模型，获得一种空间通道上下文自适应模型，来提高编码性能，而不影响其运行时间。 \(\quad\)这种模型支持预览解码和渐进解码。 introduction 学习图像压缩中最重要的 ......
Space-Channel Compression Contextual Efficient Adaptive

【CVPR2023】Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

> 论文：https://readpaper.com/paper/4728855966703960065 代码：https://github.com/ofsoundof/GRL-Image-Restoration 这个论文的代码地址叫GRL，意思是 Global, Regional, Local 的 ......
Image Hierarchies Restoration Efficient Modelling

2023ACMMM_Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing

一. Motivation 之前网络存在的缺点： 1. 使用的有限的频域信息 2. 不充足的信息交互 ： (1) 第一阶段的输出直接作为第二阶段的输入，忽略了中间特征从早期到后期的传播 (2) 在编码器解码器结构同尺度之间进行特征融合，忽略了阶段内和跨阶段的跨尺度信息交换 3. 严重的特征冗余：中间 ......
Information-driven ACMMM_Mutual Information Interaction Efficient

Proj CDeepFuzz Paper Reading: POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis

Abstract 本文： PolyCruise Method: 跨编程语言的holistic dynamic information flow analysis(DIFA) use a light language-specific analysis和language-agnostic online ......
Cross-Language Information POLYCRUISE CDeepFuzz Analysis

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

Proj CDeepFuzz Paper Reading: NYX: Greybox Hypervisor Fuzzing using Fast Snapshots and Affine Types

Abstract 背景：hypervisor(virtual machine monitor, VMM) 保障了不同虚拟机之间的安全隔离(security boundaries) 用户：攻击场景：在云服务上运行自身的VM instances, 提升权限 本文：Nyx 目的：coverage guid ......
Hypervisor CDeepFuzz Snapshots Greybox Fuzzing

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

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！ Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5331-5340, 2019 ......
Meta-Reinforcement Reinforcement Probabilistic Off-Policy Efficient

[VLDB 2012]Efficient Subgraph Matching on Billion Node Graphs

[VLDB 2012]Efficient Subgraph Matching on Billion Node Graphs 重点了解实现star-join的具体过程。 分解query和STwigs排序 文中把star叫做STwigs，每一个STwigs查询为\(q=(r, L)\)，其中r是跟节点标 ......
Efficient Subgraph Matching Billion Graphs

Proj CDeepFuzz Paper Reading: Metamorphic Testing of Deep Learning Compilers

## Abstract 背景：Compiling DNN models into high-efficiency executables is not easy: the compilation procedure often involves converting high-level model ......
Metamorphic CDeepFuzz Compilers Learning Reading

Proj CDeepFuzz Paper Reading: A Comprehensive Study of Deep Learning Compiler Bugs

## Abstract 背景：深度学习编译器处理的深度学习模型与命令式程序有根本的不同，因为深度学习模型中的程序逻辑是隐式的。（the DL models processed by DL compilers differ fundamentally from imperative programs ......
Comprehensive CDeepFuzz Compiler Learning Reading

Proj CDeepFuzz Paper Reading: DeepMutation: Mutation Testing of Deep Learning Systems

## Abstract 本文：DeepMutation Github: https://github.com/berkuva/mutation-testing-for-DNNs Task: mutation testing framework specialized for DL systems t ......
DeepMutation CDeepFuzz Mutation Learning Reading

Proj CDeepFuzz Paper Reading: Testing Deep Neural Networks

## Abstract 本文:DeepCover Github: https://github.com/TrustAI/DeepCover Task: propose 4 novel test criteria to test DNNs Method: inspired by MC/DC cover ......
CDeepFuzz Networks Reading Testing Neural

Proj CDeepFuzz Paper Reading: TensorFlow: a system for Large-Scale machine learning

## Abstract 本文:Tensorflow Github: https://github.com/tensorflow/tensorflow Task: Detail on Tensorflow dataflow model 特点: 1. operates at large scale an ......
Large-Scale TensorFlow CDeepFuzz learning Reading

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

Proj CDeepFuzz Paper Reading: PyTorch: an imperative style, high-performance deep learning library

## Abstract 本文： PyTorch Task: detail the implementation and architecture of PyTorch Github: https://github.com/pytorch/pytorch 特点： 1. PyTorch同时关注可用性和速 ......
high-performance performance imperative CDeepFuzz learning

Proj CDeepFuzz Paper Reading: Software Testing with Large Language Model: Survey, Landscape, and Vision

## Abstract 本文: Task: Review on the use of LLMs in software testing Method: 1. analyzes 52 relevant studies ## 1. Intro ![](https://img2023.cnblogs.co ......
CDeepFuzz Landscape Language Software Reading

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: Decompiling x86 Deep Neural Network Executables

## Abstract 本文: BTD github: https://github.com/monkbai/DNN-decompiler/ Task: a decompiler for DNN models to output DNN specifications including: opera ......
Decompiling Executables CDeepFuzz Network Reading

Proj CDeepFuzz Paper Reading: Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests

## Abstract 背景：In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning mod ......
Non-Deterministic Effectiveness Deterministic CDeepFuzz Balancing

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
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