Zobrazeno 1 - 10
of 926
pro vyhledávání: '"Jiang, Bowen"'
Autor:
Cheng, Haowei, Husen, Jati H., Peralta, Sien Reeve, Jiang, Bowen, Yoshioka, Nobukazu, Ubayashi, Naoyasu, Washizaki, Hironori
Context: Generative AI (GenAI) has emerged as a transformative tool in software engineering, with requirements engineering (RE) actively exploring its potential to revolutionize processes and outcomes. The integration of GenAI into RE presents both p
Externí odkaz:
http://arxiv.org/abs/2409.06741
Although end-to-end robot learning has shown some success for robot manipulation, the learned policies are often not sufficiently robust to variations in object pose or geometry. To improve the policy generalization, we introduce spatially-grounded p
Externí odkaz:
http://arxiv.org/abs/2407.08585
Autor:
Jiang, Bowen, Xie, Yangxinyu, Hao, Zhuoqun, Wang, Xiaomeng, Mallick, Tanwi, Su, Weijie J., Taylor, Camillo J., Roth, Dan
This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias. We go beyond evaluating LLMs on accuracy; rather, we aim to investigate their t
Externí odkaz:
http://arxiv.org/abs/2406.11050
Autor:
Jiang, Bowen, Xie, Yangxinyu, Wang, Xiaomeng, Su, Weijie J., Taylor, Camillo J., Mallick, Tanwi
Rationality is the quality of being guided by reason, characterized by logical thinking and decision-making that align with evidence and logical rules. This quality is essential for effective problem-solving, as it ensures that solutions are well-fou
Externí odkaz:
http://arxiv.org/abs/2406.00252
This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks. We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and co
Externí odkaz:
http://arxiv.org/abs/2403.14783
Autor:
Xie, Yangxinyu, Jiang, Bowen, Mallick, Tanwi, Bergerson, Joshua David, Hutchison, John K., Verner, Duane R., Branham, Jordan, Alexander, M. Ross, Ross, Robert B., Feng, Yan, Levy, Leslie-Anne, Su, Weijie, Taylor, Camillo J.
Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-spec
Externí odkaz:
http://arxiv.org/abs/2402.07877
This work introduces an enhanced approach to generating scene graphs by incorporating both a relationship hierarchy and commonsense knowledge. Specifically, we begin by proposing a hierarchical relation head that exploits an informative hierarchical
Externí odkaz:
http://arxiv.org/abs/2311.12889
Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior w
Externí odkaz:
http://arxiv.org/abs/2310.00156
This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of geometry as a shared representation. Unlike many contact learning appr
Externí odkaz:
http://arxiv.org/abs/2309.05832
Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in re
Externí odkaz:
http://arxiv.org/abs/2305.03942