9 citations · 14 across the 8 of their papers we have counts for
8 papers
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization
Han Guo, Ramtin Hosseini, Ruiyi Zhang +4
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning. It operates by randomly masking image patches and reconstructing the…
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy
Ramtin Hosseini, Li Zhang, Bhanu Garg +1
The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent…
ACuTE: Automatic Curriculum Transfer from Simple to Complex Environments
Yash Shukla, Christopher Thierauf, Ramtin Hosseini +2
Despite recent advances in Reinforcement Learning (RL), many problems, especially real-world tasks, remain prohibitively expensive to learn. To address this issue, several lines of…
Learning from Mistakes -- A Framework for Neural Architecture Search
Bhanu Garg, Li Zhang, Pradyumna Sridhara +3
Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In…
A Framework for Multisensory Foresight for Embodied Agents
Xiaohui Chen, Ramtin Hosseini, Karen Panetta +1
Predicting future sensory states is crucial for learning agents such as robots, drones, and autonomous vehicles. In this paper, we couple multiple sensory modalities with explorato…
DSRNA: Differentiable Search of Robust Neural Architectures
Ramtin Hosseini, Xingyi Yang, Pengtao Xie
In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neur…
Learning by Self-Explanation, with Application to Neural Architecture Search
Ramtin Hosseini, Pengtao Xie
Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this to…
Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu +1
Motivation: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computationa…