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Privacy Preserving

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

https://github.com/lamm-mit/materiomictransformer

공식 코드

Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins

https://github.com/lamp-benchmark/lamp

공식 코드

Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models

https://github.com/langnatalie/jopeq

공식 코드

Joint Privacy Enhancement and Quantization in Federated Learning

https://github.com/lars-research/adaprop

공식 코드

AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning

https://github.com/lars-research/emergnn

공식 코드

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network

https://github.com/lars-research/knowddi

공식 코드

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

https://github.com/lars-research/pacia

공식 코드

PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

https://github.com/larsbentsen/fftransformer

공식 코드

Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures

https://github.com/larsboecking/time_series_fingerprint

공식 코드

Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets

https://github.com/lascavana/objvalprediction

공식 코드

Learning optimal objective values for MILP

https://github.com/latentgnn/LatentGNN-V1-PyTorch

공식 코드

LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

https://github.com/laurencee9/structural_pertubation_detection

공식 코드

Detecting structural perturbations from time series with deep learning

https://github.com/lbcb-sci/gnns-hamiltonian-cycles

공식 코드

Finding Hamiltonian cycles with graph neural networks

https://github.com/lcai2/stea

공식 코드

A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs

https://github.com/lcosmo/DGM_pytorch

공식 코드

Differentiable Graph Module (DGM) for Graph Convolutional Networks

https://github.com/learningmatter-mit/alchemical-mlip

공식 코드

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials

https://github.com/learnware-lamda/learnware

공식 코드

Learnware of Language Models: Specialized Small Language Models Can Do Big

https://github.com/lebyni/PFA

공식 코드

PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

https://github.com/leijiezhang/pp_hfnn

공식 코드

Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data

https://github.com/leilynourbakhsh/inf2guard

공식 코드

Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks

https://github.com/leolouis14/kucnet

공식 코드

Knowledge-Enhanced Recommendation with User-Centric Subgraph Network

https://github.com/leosampaio/scene-designer

공식 코드

Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch

https://github.com/leson502/CORECT_EMNLP2023

공식 코드

Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction

https://github.com/lexpk/logicaldistillationofgnns

공식 코드

Logical Distillation of Graph Neural Networks

https://github.com/lgalke/lifelong-learning

공식 코드

Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes

https://github.com/li-jicheng/mmasd-a-multimodal-dataset-for-autism-intervention-analysis

공식 코드

MMASD: A Multimodal Dataset for Autism Intervention Analysis

https://github.com/liangzid/MERGE

공식 코드

MERGE: Fast Private Text Generation

https://github.com/liaochunyang/DPRFM

공식 코드

Differentially Private Random Feature Model

https://github.com/lidonghao1996/neuromixgdp

공식 코드

NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release

https://github.com/lightbulb128/pencil

공식 코드

Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption

https://github.com/lijunxian111/UIGNN

공식 코드

Uncertainty-aware Traffic Prediction under Missing Data

https://github.com/likojack/odam

공식 코드

ODAM: Object Detection, Association, and Mapping using Posed RGB Video

https://github.com/lili1013/fupm

공식 코드

Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation

https://github.com/limresgrp/herobm

공식 코드

HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations

https://github.com/lingjuanlv/FPPDL

공식 코드

Towards Fair and Privacy-Preserving Federated Deep Models

https://github.com/lins-lab/client2vec

공식 코드

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing

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