Papers with Code 아카이브

Privacy Preserving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/khoaguin/split-learning-1D-HE

공식 코드

Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning

https://github.com/khushnood/TempNodeEmbed_upload

공식 코드

TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix

https://github.com/khy0809/fewshot-egnn

공식 코드

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/ki-research-institute/external-evaluation

공식 코드

Estimating Model Performance on External Samples from Their Limited Statistical Characteristics

https://github.com/kiananvari/mtms-a-domain-adaptive-meta-knowledge-distillation-model-for-medical-image-classification

공식 코드

Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

https://github.com/kierensharma/fluxgat

공식 코드

FluxGAT: Integrating Flux Sampling with Graph Neural Networks for Unbiased Gene Essentiality Classification

https://github.com/kingformatty/NUSD

공식 코드

Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals

https://github.com/kirschte/sbdt

공식 코드

S-BDT: Distributed Differentially Private Boosted Decision Trees

https://github.com/kiyalab-tmu/secretkeyvoiceprivacypreserving-cnn

공식 코드

Speech privacy-preserving methods using secret key for convolutional neural network models and their robustness evaluation

https://github.com/kkhuang81/UniFilter

공식 코드

How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

https://github.com/kkteru/grail

공식 코드

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/kleincup/MGMN

공식 코드

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

https://github.com/knowledgedefinednetworking/demo-routenet

공식 코드

RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

https://github.com/kolejnyy/ANYCQ

공식 코드

One Model, Any Conjunctive Query: Graph Neural Networks for Answering Complex Queries over Knowledge Graphs

https://github.com/konsotirop/adamm

공식 코드

ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach

https://github.com/konstantinklemmer/geopointgan

공식 코드

GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy

https://github.com/koustav123/aesthetics_assessment_using_graphs

공식 코드

Image Aesthetics Assessment Using Graph Attention Network

https://github.com/kracr/reonto-relation-extraction

공식 코드

ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction

https://github.com/kris927b/JobStack

공식 코드

De-identification of Privacy-related Entities in Job Postings

https://github.com/krishnap25/RFA

공식 코드

Robust Aggregation for Federated Learning

https://github.com/krishnaswamylab/dymag

공식 코드

DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms

https://github.com/kschwethelm/dpal

공식 코드

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy

https://github.com/ksgr5566/unsegarmanet

공식 코드

UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters

https://github.com/kumarrobotics/coveragecontrol

공식 코드

LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control

https://github.com/kunalchelani/Line2Point

공식 코드

How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines

https://github.com/kunalchelani/objectpositioningfromposes

공식 코드

Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses

https://github.com/kunzhan/brainguard

공식 코드

BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities

https://github.com/kunzhan/sdsne

공식 코드

Stationary Diffusion State Neural Estimation for Multiview Clustering

https://github.com/kvgarimella/sisyphus-ppml

공식 코드

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

https://github.com/kxchern/dkepool-tsa

공식 코드

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

https://github.com/kyle-one/signa

공식 코드

Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification

https://github.com/kyleenliao/Prescribed_Fire_PM2.5_Simulation

공식 코드

Simulating the Air Quality Impact of Prescribed Fires Using Graph Neural Network-Based PM$_{2.5}$ Forecasts

https://github.com/kyonofx/mlcgmd

공식 코드

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks

https://github.com/laats/subbotinmechanism

공식 코드

Differential privacy for symmetric log-concave mechanisms

https://github.com/lachlanlin/pricdsr

공식 코드

Privacy-Preserving Cross-Domain Sequential Recommendation

https://github.com/laiweijiang/VAGNN

공식 코드

A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.