interpretability-and-explainability

There are 1 repository under interpretability-and-explainability topic.

HennyJie / IBGNN
MICCAI 2022 (Oral): Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
brain graph-neural-networks healthcare interpretability-and-explainability miccai2022
Language:Python 49
Wuyxin / DISC
Official code of "Discover and Cure: Concept-aware Mitigation of Spurious Correlation" (ICML 2023)
data-augmentation generalization icml-2023 interpretability-and-explainability stable-diffusion spurious-correlation pytorch
Language:Python 34
WanyuGroup / CVPR2022-OrphicX
Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks."
causality graph-neural-networks interpretability-and-explainability
Language:Python 33
liugangcode / GREA
[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"
graph-neural-networks molecular-property-prediction rationalization polymer-property-prediction interpretability-and-explainability
Language:Python 32
cwangrun / ST-ProtoPNet
[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification
fine-grained-classification interpretability-and-explainability interpretable-ai interpretable-deep-learning interpretable-neural-networks image-recognition explainable-ai
Language:Python 17
interpretable-ml-class / interpretable-ml-class.github.io
Explainable AI: From Simple Rules to Complex Generative Models
ai explainability explainable-ai explainable-ml interpretability interpretability-and-explainability ml
Language:HTML 7
VictorNico / NNs_from_scratch
Build a Neural net from scratch without keras or pytorch just by using numpy for calculus, pandas for data loading.
docstrings eda gradiantdescent interpretability-and-explainability mnist neural-network numpy oop-principles pandas simulated-annealing-algorithm
Language:Jupyter Notebook 2
DimitrisReppas / On_visual_explanation_of_supervised_and_self-supervised_learning
Visualization methods to interpret CNNs and Vision Transformers, trained in a supervised or self-supervised way. The methods are based on CAM or on the attention mechanism of Transformers. The results are evaluated qualitatively and quantitatively.
cnns computer-vision deep-neural-networks explainable-ai interpretability-and-explainability self-supervised-learning supervised-learning transformers
Language:Python 1
Imenbaa / BA-LR
Explainable Speaker Recognition
forensics automatic-voice-comparison forensic-speaker-recognition interpretability-and-explainability resnet34 x-vector-pytorch likelihood-ratio speaker-recognition
Language:Python 1
goz1985 / RST-ARM-GLM_-Research
Work on combining Logit model with an information granulation method for better interpretability
bagging-ensemble boosting-ensemble glm interpretability-and-explainability rough-sets
Language:R 0
MattScicluna / interpretable_tsne
Implementation of the gradient-based t-SNE sttribution method described in our GLBIO oral presentation: 'Towards Computing Attributions for Dimensionality Reduction Techniques'
dimensionality-reduction interpretability-and-explainability research-tool
Language:Python 0
Skyyyy0920 / FGAI
gnns graph-neural-networks interpretability-and-explainability
Language:Python 0
Skyyyy0920 / SSCBM
Semi-supervised Concept Bottleneck Models (SSCBM)
concept-bottleneck-models interpretability-and-explainability
Language:Python