1 code implementation • 9 Mar 2021 • Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, Xi Peng
A common assumption in multimodal learning is the completeness of training data, i. e., full modalities are available in all training examples.
1 code implementation • CVPR 2023 • Tang Li, Fengchun Qiao, Mengmeng Ma, Xi Peng
How to develop robust explanations against out-of-distribution data?
no code implementations • 23 Sep 2014 • Bo Han, Bo He, Tingting Sun, Mengmeng Ma, Amaury Lendasse
By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in.
no code implementations • 9 Aug 2014 • Bo Han, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, Amaury Lendasse
It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.
no code implementations • 9 Aug 2014 • Bo Han, Bo He, Rui Nian, Mengmeng Ma, Shujing Zhang, Minghui Li, Amaury Lendasse
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data.
no code implementations • CVPR 2022 • Mengmeng Ma, Jian Ren, Long Zhao, Davide Testuggine, Xi Peng
Based on these findings, we propose a principle method to improve the robustness of Transformer models by automatically searching for an optimal fusion strategy regarding input data.