Summary of Reconstruction Attacks on Machine Unlearning: Simple Models Are Vulnerable, by Martin Bertran et al.
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerableby Martin Bertran, Shuai Tang, Michael Kearns,…
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerableby Martin Bertran, Shuai Tang, Michael Kearns,…
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflectionsby Massimo Bini, Karsten Roth, Zeynep Akata,…
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSAby Siva Uday Sampreeth Chebolu, Franck…
Learning Latent Graph Structures and their Uncertaintyby Alessandro Manenti, Daniele Zambon, Cesare AlippiFirst submitted to…
BAN: Detecting Backdoors Activated by Adversarial Neuron Noiseby Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian…
Exploring Diffusion Models’ Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networksby Xiaoyu…
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplersby Kiyoung Seong, Seonghyun Park,…
Domain Adaptation with Cauchy-Schwarz Divergenceby Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke,…
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classificationby Hossam M. Zawbaa, Wael Rashwan,…
MM-Lego: Modular Biomedical Multimodal Models with Minimal Fine-Tuningby Konstantin Hemker, Nikola Simidjievski, Mateja JamnikFirst submitted…