Specification Curve is a Python package that performs specification curve analysis: exploring how a coefficient varies under multiple different specifications of a statistical model.
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Updated
May 24, 2024 - Python
Specification Curve is a Python package that performs specification curve analysis: exploring how a coefficient varies under multiple different specifications of a statistical model.
👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
[IEEE TMI 2024] Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
Controlling the spectral norm of implicitly linear layers (e.g., convolutional layers)
[IVS'24] UniBEV: the official implementation of UniBEV
[CVPR 2024] Source code for "Diffusion-Based Adaptation for Classification of Unknown Degraded Images".
An unofficial version of the PyTorch implementation of CURE and Fast Adversarial training with FGSM.
DPLL(T)-based Verification tool for DNNs
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
Characterizing Data Point Vulnerability via Average-Case Robustness, UAI 2024
[ICCV 2023] Robust Object Modeling for Visual Tracking, Official Implementation
A unified evaluation framework for large language models
Unofficial pytorch implementation of Fourier Heat Map proposed in 'A Fourier Perspective on Model Robustness in Computer Vision' [Yin+, NeurIPS2019]
Uses the simplex to propose a tighter boundary for the l1 perturbation of the convex activation function network, improving the effect of the CROWN algorithm.
Revisiting Test Time Adaptation Under Online Evaluation
[CVPR2024 Highlight] Official Code for "ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object"
measures the vulnerability of the system by formulating the aggregated metric using extended metrics.
Code for the paper "Multi-scale Diffusion Denoised Smoothing" (NeurIPS 2023)
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