359 citations · 2k across the 127 of their papers we have counts for
6 papers · 1 filter
Large language models improve Alzheimer's disease diagnosis using multi-modality data
Yingjie Feng, Jun Wang, Xianfeng Gu +2
In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference. Non-imaging patient data such as patient information, genetic data, medica…
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting
Hongyao Tang, Min Zhang, Jianye Hao
Knowing the learning dynamics of policy is significant to unveiling the mysteries of Reinforcement Learning (RL). It is especially crucial yet challenging to Deep RL, from which th…
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Xingwu Guo, Ziwei Zhou, Yueling Zhang +2
Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments…
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes
Min Zhang, Hongyao Tang, Jianye Hao +1
Lying on the heart of intelligent decision-making systems, how policy is represented and optimized is a fundamental problem. The root challenge in this problem is the large scale a…
A Survey on Dropout Methods and Experimental Verification in Recommendation
Yangkun Li, Weizhi Ma, Chong Chen +4
Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of cop…
R-Drop: Regularized Dropout for Neural Networks
Xiaobo Liang, Lijun Wu, Juntao Li +6
Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in mod…