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Search results for python trustworthy machine learning
python
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trustworthy-machine-learning
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14 search results found
Backdoorbox
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325
The open-sourced Python toolbox for backdoor attacks and defenses.
Trustllm
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164
TrustLLM: Trustworthiness in Large Language Models
Torch Uncertainty
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132
Uncertainty for deep learning models in PyTorch 🌱
Infairness
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58
PyTorch package to train and audit ML models for Individual Fairness
Entropic Out Of Distribution Detection
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47
Add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
Flat
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46
[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
Distinction Maximization Loss
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34
Improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
Contrxt
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19
a tool for comparing the predictions of any text classifiers
Merlin
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14
MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.
Avg Avg
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11
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Negative Label Smoothing
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10
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
Robust Deep Learning
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9
Train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
Morphence
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6
Morphence: An implementation of a moving target defense against adversarial example attacks demonstrated for image classification models trained on MNIST and CIFAR10.
Trustworthy Ai Fetal Brain Segmentation
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6
Trustworthy AI method based on Dempster-Shafer theory - application to fetal brain 3D T2w MRI segmentation
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