no code implementations • 17 Apr 2024 • Yaqi Xie, Will Ma, Linwei Xin
Second, the number of parameters in a policy class may not be the correct measure of overfitting error: in fact, the class of policies defined by T time-varying base-stock levels exhibits a generalization error comparable to that of the two-parameter (s, S) policy class.
2 code implementations • 5 Apr 2024 • Zifu Wan, Yuhao Wang, Silong Yong, Pingping Zhang, Simon Stepputtis, Katia Sycara, Yaqi Xie
In this work, we introduce Sigma, a Siamese Mamba network for multi-modal semantic segmentation, utilizing the Selective Structured State Space Model, Mamba.
no code implementations • 28 Mar 2024 • Yaqi Xie, Anjali Rawal, Yujing Cen, Dixuan Zhao, Sunil K Narang, Shanu Sushmita
As advanced modern systems like deep neural networks (DNNs) and generative AI continue to enhance their capabilities in producing convincing and realistic content, the need to distinguish between user-generated and machine generated content is becoming increasingly evident.
no code implementations • 26 Mar 2024 • Samuel Li, Sarthak Bhagat, Joseph Campbell, Yaqi Xie, Woojun Kim, Katia Sycara, Simon Stepputtis
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments.
1 code implementation • 19 Mar 2024 • Ce Zhang, Simon Stepputtis, Katia Sycara, Yaqi Xie
Recently, large-scale pre-trained Vision-Language Models (VLMs) have demonstrated great potential in learning open-world visual representations, and exhibit remarkable performance across a wide range of downstream tasks through efficient fine-tuning.
1 code implementation • 18 Mar 2024 • Ce Zhang, Simon Stepputtis, Joseph Campbell, Katia Sycara, Yaqi Xie
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches.
no code implementations • 14 Dec 2023 • Yafei Hu, Quanting Xie, Vidhi Jain, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Shibo Zhao, Yu Quan Chong, Chen Wang, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Zsolt Kira, Fei Xia, Yonatan Bisk
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.
no code implementations • 9 Nov 2023 • Simon Stepputtis, Joseph Campbell, Yaqi Xie, Zhengyang Qi, Wenxin Sharon Zhang, Ruiyi Wang, Sanketh Rangreji, Michael Lewis, Katia Sycara
We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation.
1 code implementation • 10 Feb 2023 • Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, Harold Soh
Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains.
no code implementations • 28 Jan 2021 • Yaqi Xie, Fan Zhou, Harold Soh
However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction.
1 code implementation • NeurIPS 2019 • Yaqi Xie, Ziwei Xu, Mohan S. Kankanhalli, Kuldeep S. Meel, Harold Soh
Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.
no code implementations • 3 Sep 2019 • Yaqi Xie, Indu P Bodala, Desmond C. Ong, David Hsu, Harold Soh
In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions.