llmondo's starred repositories
Interpretability
Resources for Machine Learning Explainability
graph-fraud-detection-papers
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
distribution_inference
Code for our paper 'Formalizing Distribution Inference Risks'
awesome-graph-explainability-papers
Papers about explainability of GNNs
pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
WHU_FinTech_Workshop
武汉大学金融科技研讨班
ProtoPNet
This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).
Convolutional-Prototype-Learning
An implementation (TensorFlow) of CPL and GCPL appeared in CVPR2018 paper: "Robust Classification with Convolutional Prototype Learning"
Multi-GCGRU
The code and datasets of "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction"
Video-Summarization-using-Keyframe-Extraction-and-Video-Skimming
Experimenting with different Summarizing techniques on SumMe Dataset
superframes
Code for superframe extraction from video frames based on paper- Semantic Text Summarization of Long Videos at 2017 IEEE Winter Conference on Applications of Computer Vision
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
membership-inference
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
piecewise_linear_fit_py
fit piecewise linear data for a specified number of line segments