Zobrazeno 1 - 10
of 3 929
pro vyhledávání: '"Ranasinghe, P."'
Autor:
Li, Xiang, Mata, Cristina, Park, Jongwoo, Kahatapitiya, Kumara, Jang, Yoo Sung, Shang, Jinghuan, Ranasinghe, Kanchana, Burgert, Ryan, Cai, Mu, Lee, Yong Jae, Ryoo, Michael S.
Large Language Models (LLMs) equipped with extensive world knowledge and strong reasoning skills can tackle diverse tasks across domains, often by posing them as conversation-style instruction-response pairs. In this paper, we propose LLaRA: Large La
Externí odkaz:
http://arxiv.org/abs/2406.20095
This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes m
Externí odkaz:
http://arxiv.org/abs/2407.01596
Autor:
Park, Jongwoo, Ranasinghe, Kanchana, Kahatapitiya, Kumara, Ryoo, Wonjeong, Kim, Donghyun, Ryoo, Michael S.
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely-related. Therefore, when performing long-form video question answering (LVQA),all infor
Externí odkaz:
http://arxiv.org/abs/2406.09396
Autor:
Ranasinghe, Kuranage Roche Rayan, Rou, Hyeon Seok, de Abreu, Giuseppe Thadeu Freitas, Takahashi, Takumi, Ito, Kenta
We propose new schemes for joint channel and data estimation (JCDE) and radar parameter estimation (RPE) in doubly-dispersive channels, such that integrated sensing and communications (ISAC) is enabled by user equipment (UE) independently performing
Externí odkaz:
http://arxiv.org/abs/2405.16945
Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision mak
Externí odkaz:
http://arxiv.org/abs/2405.02605
The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to de
Externí odkaz:
http://arxiv.org/abs/2404.11470
Autor:
Ranasinghe, Kanchana, Shukla, Satya Narayan, Poursaeed, Omid, Ryoo, Michael S., Lin, Tsung-Yu
Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA). However, existing V-LLMs (e.g. BL
Externí odkaz:
http://arxiv.org/abs/2404.07449
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. Sparse attacks aim to discover a minimum number-the l0 bounded-perturbations to model inputs to c
Externí odkaz:
http://arxiv.org/abs/2404.05311
Autor:
Raihan, Nishat, Goswami, Dhiman, Puspo, Sadiya Sayara Chowdhury, Newman, Christian, Ranasinghe, Tharindu, Zampieri, Marcos
Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGP
Externí odkaz:
http://arxiv.org/abs/2404.02540
Autor:
Doan, Bao Gia, Nguyen, Dang Quang, Montague, Paul, Abraham, Tamas, De Vel, Olivier, Camtepe, Seyit, Kanhere, Salil S., Abbasnejad, Ehsan, Ranasinghe, Damith C.
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at t
Externí odkaz:
http://arxiv.org/abs/2403.18309