influence-functions
There are 20 repositories under influence-functions topic.
nimarb/pytorch_influence_functions
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
aai-institute/pyDVL
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
alstonlo/torch-influence
A simple PyTorch implementation of influence functions.
pomonam/kronfluence
Influence Functions with (Eigenvalue-corrected) Kronecker-Factored Approximate Curvature
bsharchilev/influence_boosting
Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"
RyanWangZf/Influence_Subsampling
Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
deel-ai/influenciae
đź‘‹ Influenciae is a Tensorflow Toolbox for Influence Functions
feifeiobama/InfluenceCL
[CVPR 2023] Regularizing Second-Order Influences for Continual Learning
jjbrophy47/tree_influence
Influence Estimation for Gradient-Boosted Decision Trees
HKUNLP/ProGen
[EMNLP-2022 Findings] Code for paper “ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback”.
sail-sg/D-TRAK
Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)
pomonam/jax-influence
A simple Jax implementation of influence functions.
ryokamoi/pytorch_influence_functions
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
xszheng2020/memorization
An Empirical Study of Memorization in NLP (ACL 2022)
ShinKyuY/Understanding_Black_box_Predictions_via_Influence_Functions_tutorial_MNIST
Tiny Tutorial on https://arxiv.org/abs/1703.04730
hslyu/GIF
Official implementation of "Deeper Understanding of Black-box Predictions via Generalized Influence Functions".
MortonWang/geo_IF
This is an implementation of the paper ”Interpreting Twitter User Geolocation“.
sungyubkim/if_classic
A brief notebook on Influence Function (IF) for classical generative models (e.g., k-NN, KDE, GMM)
ICDM-Lab/geo_IF
This repo provides an implementation of the paper Interpreting Twitter User Geolocation.
ajsanjoaquin/pytorch_influence_functions
This is a [Stable] PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.