Zobrazeno 1 - 10
of 53 148
pro vyhledávání: '"Senthil, A."'
Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form responses to open-ended queries is challenging as it requires visually veri
Externí odkaz:
http://arxiv.org/abs/2410.13121
Autor:
Stein, Alex, Sharpe, Samuel, Bergman, Doron, Kumar, Senthil, Bruss, Bayan, Dickerson, John, Goldstein, Tom, Goldblum, Micah
Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what rating a customer will assign a product on a retail platform. Existing a
Externí odkaz:
http://arxiv.org/abs/2410.10648
Synthetic platforms afford an unparalleled degree of controllability in realizing strongly-correlated phases of matter. In this work, we study the possibility of electrically tunable exciton-mediated superconductivity arising in charge-imbalanced bil
Externí odkaz:
http://arxiv.org/abs/2410.09148
Autor:
Ming, Yifei, Purushwalkam, Senthil, Pandit, Shrey, Ke, Zixuan, Nguyen, Xuan-Phi, Xiong, Caiming, Joty, Shafiq
Ensuring faithfulness to context in large language models (LLMs) and retrieval-augmented generation (RAG) systems is crucial for reliable deployment in real-world applications, as incorrect or unsupported information can erode user trust. Despite adv
Externí odkaz:
http://arxiv.org/abs/2410.03727
Autor:
Shi, Zhengyan Darius, Senthil, T.
We study novel itinerant phases that can be accessed by doping a fractional quantum anomalous Hall (FQAH) insulator, with a focus on the experimentally observed Jain states at lattice filling $\nu = p/(2p+1)$. Unlike in the lowest Landau level, where
Externí odkaz:
http://arxiv.org/abs/2409.20567
Autor:
Weerakoon, Kasun, Elnoor, Mohamed, Seneviratne, Gershom, Rajagopal, Vignesh, Arul, Senthil Hariharan, Liang, Jing, Jaffar, Mohamed Khalid M, Manocha, Dinesh
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM) and categorizes th
Externí odkaz:
http://arxiv.org/abs/2409.16484
Autor:
Nguyen, Xuan-Phi, Pandit, Shrey, Purushwalkam, Senthil, Xu, Austin, Chen, Hailin, Ming, Yifei, Ke, Zixuan, Savarese, Silvio, Xong, Caiming, Joty, Shafiq
Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI. The LLMs used in RAG applica
Externí odkaz:
http://arxiv.org/abs/2409.09916
Publikováno v:
International Journal of Ad Hoc and Ubiquitous Computing Vol. 47, No. 1, Year: 2024
Robots find extensive applications in industry. In recent years, the influence of robots has also increased rapidly in domestic scenarios. The Q-learning algorithm aims to maximise the reward for reaching the goal. This paper proposes a modified vers
Externí odkaz:
http://arxiv.org/abs/2409.01046
We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications models or error control coding. This work relies solely on the tools of
Externí odkaz:
http://arxiv.org/abs/2409.01129
The way we engage with digital spaces and the digital world has undergone rapid changes in recent years, largely due to the emergence of the Metaverse. As technology continues to advance, the demand for sophisticated and immersive interfaces to inter
Externí odkaz:
http://arxiv.org/abs/2409.00615