Summary of Don’t Label Twice: Quantity Beats Quality When Comparing Binary Classifiers on a Budget, by Florian E. Dorner and Moritz Hardt
Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budgetby Florian E.…
Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budgetby Florian E.…
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approachby Tianshi Wang,…
Selecting Large Language Model to Fine-tune via Rectified Scaling Lawby Haowei Lin, Baizhou Huang, Haotian…
Future Directions in the Theory of Graph Machine Learningby Christopher Morris, Fabrizio Frasca, Nadav Dym,…
Your Diffusion Model is Secretly a Certifiably Robust Classifierby Huanran Chen, Yinpeng Dong, Shitong Shao,…
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learningby Weiqi Fu, Lianming…
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysisby Pankaj…
On the Exploitation of DCT-Traces in the Generative-AI Domainby Orazio Pontorno, Luca Guarnera, Sebastiano BattiatoFirst…
Unlearnable Examples For Time Seriesby Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James BaileyFirst submitted…
Alpha-divergence loss function for neural density ratio estimationby Yoshiaki KitazawaFirst submitted to arxiv on: 3…