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attack_classification_models_with_transferability icon attack_classification_models_with_transferability

Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet, CVPR2021 安全AI挑战者计划第六期:ImageNet无限制对抗攻击 决赛第四名(team name: Advers)

autokernel icon autokernel

AutoKernel 是一个简单易用,低门槛的自动算子优化工具,提高深度学习算法部署效率。

bisheng icon bisheng

Bisheng is an open LLM devops platform for next generation AI applications.

datetime233 icon datetime233

DateTime233 is a zero-designed DateTime API by flux-style. 重新设计的整套 DateTime API~

dgiot icon dgiot

Open source platform for IoT,30 min Quick Deployment,10M-level device connection,Carrier level Stability;物联网开源平台,30分钟快速部署,千万级承载,电信级稳定性. Zero code for Object model-Rule Engine-Data Channel-Configuration Page; Fully open source, Multi industrial protocols are compatible.物模型-规则引擎-数据通道-组态页面全流程零代码开发、前后端完全开源、多行业工业协议兼容。

ffmpeg-plus-opengl icon ffmpeg-plus-opengl

🔥 OpenGL Filter for FFmpeg: Render video by your shaders(GLSL) | FFmpeg Filter支持OpenGL Shader和LUT 渲染滤镜。

gundam icon gundam

GUNDAM is a data management system that prioritizes data using language models.

hyphen icon hyphen

Course Full Stack Java developer - Java + JSP + Restful WS + Spring on udemy

lakesoul icon lakesoul

A Table Structure Storage on Data Lakes to Unify Batch and Streaming Data Processing

object-detection icon object-detection

Learning object detection segmentation with a DRL-based feature matching agent

paddlerec icon paddlerec

Recommendation Algorithm大规模推荐算法库,包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise等,包含经典推荐系统数据集criteo 、movielens等

reactuse icon reactuse

Collection of essential React Hooks Utilities.

ufo icon ufo

A UI-Focused Agent for Windows OS Interaction.

understanding-vits icon understanding-vits

In this repository, you will learn how code works in VITS(Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech) in Jupyter Notebooks, including normalizing data, training process, inference process, and model's details.

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