Zobrazeno 1 - 10
of 2 707
pro vyhledávání: '"Shreyas, S."'
Autor:
R, Shreyas S
Q-learning is a widely used algorithm in reinforcement learning (RL), but its convergence can be slow, especially when the discount factor is close to one. Successive Over-Relaxation (SOR) Q-learning, which introduces a relaxation factor to speed up
Externí odkaz:
http://arxiv.org/abs/2409.06356
Autor:
R, Shreyas S, Vijesh, Antony
An interesting iterative procedure is proposed to solve a two-player zero-sum Markov games. Under suitable assumption, the boundedness of the proposed iterates is obtained theoretically. Using results from stochastic approximation, the almost sure co
Externí odkaz:
http://arxiv.org/abs/2407.04240
Autor:
Vijesh, Antony, R, Shreyas S
Q-learning is a stochastic approximation version of the classic value iteration. The literature has established that Q-learning suffers from both maximization bias and slower convergence. Recently, multi-step algorithms have shown practical advantage
Externí odkaz:
http://arxiv.org/abs/2407.02369
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:
Joglekar, Shreyas S., Baumgaertl, Korbinian, Mucchietto, Andrea, Berger, Francis, Grundler, Dirk
Spin waves (magnons) can enable wave-based neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. In this study, we explore the storage of magnon signals and the magne
Externí odkaz:
http://arxiv.org/abs/2312.09177
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
Recent advancements in large language models (LLMs) have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and general reasoning abilities of LLMs obtained during pretraining. However
Externí odkaz:
http://arxiv.org/abs/2309.09919
Autor:
Alkan, Cagan, Mardani, Morteza, Liao, Congyu, Li, Zhitao, Vasanawala, Shreyas S., Pauly, John M.
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we
Externí odkaz:
http://arxiv.org/abs/2306.02888
Autor:
Oscanoa, Julio A., Ong, Frank, Iyer, Siddharth S., Li, Zhitao, Sandino, Christopher M., Ozturkler, Batu, Ennis, Daniel B., Pilanci, Mert, Vasanawala, Shreyas S.
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. One common approach is to reduce th
Externí odkaz:
http://arxiv.org/abs/2305.06482
Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for
Externí odkaz:
http://arxiv.org/abs/2211.04703