Asia
Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval
Vision and diverse languages are important information sources in our living world.
DetNAS: Backbone Search for Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang, GAOFENG MENG, Xinyu Xiao, Jian Sun
Neural Information Processing Systems http://nips.cc/
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni
Neural Information Processing Systems http://nips.cc/
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models Jiaqi Li
Machine unlearning (MU) empowers individuals with the'right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the leaked visual data of concepts.
bc218a0c656e49d4b086975a9c785f47-Supplemental-Datasets_and_Benchmarks.pdf
Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.
Labeling Neural Representations with Inverse Recognition
The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [
